Patent Publication Number: US-2020285649-A1

Title: Systems and methods for a machine learning framework

Description:
TECHNICAL FIELD 
     This disclosure relates generally to creation and management of machine learning algorithms, and more particularly relates to systems and methods for a framework for the creation and management of machine learning algorithms. 
     BACKGROUND 
     The recent rise of big data has led many organizations to use machine learning algorithms in their practice. This rapid adoption of machine learning algorithms, though, presents a number of problems inherent in machine learning algorithms, and therefore problems confined to computer systems. First, while application of machine learning algorithms can be beneficial for an organization, the startup costs can be very high for a number of reasons. 
     Building a machine learning algorithm is time, labor, and computing resource intensive. Raw data needs to be standardized and converted into a workable format for each algorithm created. Labels for the data also need to be identified from the standardized raw data for each algorithm created. Different mathematical models then need to be trained on this labeled data to determine which mathematical model gives the best outcomes. 
     Each of the steps outlined in the paragraph above involve the creation of bespoke computer programs for the specific task involved with the creation of the specific model. Further, oftentimes these computer programs are so customized, that they cannot be reused when a similar machine learning model is generated. This result, then, leads data scientists and computer system engineers spending large amounts of time creating custom-made programs to train a single machine learning algorithm. There is a need, then, for a system and/or method for a machine learning framework that minimizes the coding time required to create a machine learning model. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       To facilitate further description of the embodiments, the following drawings are provided in which: 
         FIG. 1  illustrates a front elevational view of a computer system that is suitable for implementing various embodiments of the systems disclosed in  FIGS. 3 and 5 ; 
         FIG. 2  illustrates a representative block diagram of an example of the elements included in the circuit boards inside a chassis of the computer system of  FIG. 1 ; 
         FIG. 3  illustrates a representative block diagram of a system, according to an embodiment; 
         FIG. 4  illustrates a representative block diagram of a system, according to an embodiment; 
         FIG. 5  illustrates a representative block diagram of a system, according to an embodiment; 
         FIG. 6  illustrates a representative block diagram of a system, according to an embodiment; 
         FIG. 7  illustrates a flowchart for a method, according to certain embodiments; 
         FIG. 8  illustrates a flowchart for a method, according to certain embodiments; 
         FIG. 9  illustrates a representative block diagram of a system, according to an additional embodiment; 
         FIG. 10  illustrates a representative block diagram of a system, according to an additional embodiment; 
         FIG. 11  illustrates a representative embodiment of a configuration file, according to an additional embodiment; and 
         FIG. 12  illustrates a representative embodiment of a configuration file, according to an additional embodiment. 
     
    
    
     For simplicity and clarity of illustration, the drawing figures illustrate the general manner of construction, and descriptions and details of well-known features and techniques may be omitted to avoid unnecessarily obscuring the present disclosure. Additionally, elements in the drawing figures are not necessarily drawn to scale. For example, the dimensions of some of the elements in the figures may be exaggerated relative to other elements to help improve understanding of embodiments of the present disclosure. The same reference numerals in different figures denote the same elements. 
     The terms “first,” “second,” “third,” “fourth,” and the like in the description and in the claims, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the terms so used are interchangeable under appropriate circumstances such that the embodiments described herein are, for example, capable of operation in sequences other than those illustrated or otherwise described herein. Furthermore, the terms “include,” and “have,” and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, device, or apparatus that comprises a list of elements is not necessarily limited to those elements, but may include other elements not expressly listed or inherent to such process, method, system, article, device, or apparatus. 
     The terms “left,” “right,” “front,” “back,” “top,” “bottom,” “over,” “under,” and the like in the description and in the claims, if any, are used for descriptive purposes and not necessarily for describing permanent relative positions. It is to be understood that the terms so used are interchangeable under appropriate circumstances such that the embodiments of the apparatus, methods, and/or articles of manufacture described herein are, for example, capable of operation in other orientations than those illustrated or otherwise described herein. 
     The terms “couple,” “coupled,” “couples,” “coupling,” and the like should be broadly understood and refer to connecting two or more elements mechanically and/or otherwise. Two or more electrical elements may be electrically coupled together, but not be mechanically or otherwise coupled together. Coupling may be for any length of time, e.g., permanent or semi-permanent or only for an instant. “Electrical coupling” and the like should be broadly understood and include electrical coupling of all types. The absence of the word “removably,” “removable,” and the like near the word “coupled,” and the like does not mean that the coupling, etc. in question is or is not removable. 
     As defined herein, two or more elements are “integral” if they are comprised of the same piece of material. As defined herein, two or more elements are “non-integral” if each is comprised of a different piece of material. 
     As defined herein, “real-time” can, in some embodiments, be defined with respect to operations carried out as soon as practically possible upon occurrence of a triggering event. A triggering event can include receipt of data necessary to execute a task or to otherwise process information. Because of delays inherent in transmission and/or in computing speeds, the term “real time” encompasses operations that occur in “near” real time or somewhat delayed from a triggering event. In a number of embodiments, “real time” can mean real time less a time delay for processing (e.g., determining) and/or transmitting data. The particular time delay can vary depending on the type and/or amount of the data, the processing speeds of the hardware, the transmission capability of the communication hardware, the transmission distance, etc. However, in many embodiments, the time delay can be less than approximately one second, two seconds, five seconds, or ten seconds. 
     As defined herein, “approximately” can, in some embodiments, mean within plus or minus ten percent of the stated value. In other embodiments, “approximately” can mean within plus or minus five percent of the stated value. In further embodiments, “approximately” can mean within plus or minus three percent of the stated value. In yet other embodiments, “approximately” can mean within plus or minus one percent of the stated value. 
     DESCRIPTION OF EXAMPLES OF EMBODIMENTS 
     A number of embodiments can include a system. The system can include one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors. The computing instructions can be configured to run on the one or more processors and perform acts of receiving, from an extraction, transform, load (ETL) application, a matrix, which can comprise raw feature data; storing the matrix comprising the raw feature data in a standard format in the one or more non-transitory computer readable storage devices; receiving a configuration file over a computer network; storing the configuration file in a standard format in the one or more non-transitory computer readable storage devices; instantiating one or more unifier applications based upon the configuration file; identifying relevant feature data of the raw feature data; storing the relevant feature data in a standardized format in an output file in the one or more non-transitory computer-readable storage devices; and transmitting, over the computer network in real time to a model building system, the output file comprising the relevant feature data from the one or more non-transitory computer-readable storage devices, so that the machine learning model building applications have immediate access to up-to-date data. 
     Various embodiments include a method. The method can include receiving, from an extraction, transform, load (ETL) application, a matrix, which can comprise raw feature data; storing the matrix comprising the raw feature data in a standard format in the one or more non-transitory computer readable storage devices; receiving a configuration file over a computer network; storing the configuration file in a standard format in the one or more non-transitory computer readable storage devices; instantiating one or more unifier applications based upon the configuration file; identifying relevant feature data of the raw feature data; storing the relevant feature data in a standardized format in an output file in the one or more non-transitory computer-readable storage devices; and transmitting, over the computer network in real time to a model building system, the output file comprising the relevant feature data from the one or more non-transitory computer-readable storage devices, so that the machine learning model building applications have immediate access to up-to-date data. 
     A number of embodiments can include a system. The system can include one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors. The computing instructions can be configured to run on the one or more processors and perform acts of creating a configuration file which can comprise: one or more attributes defining a machine learning algorithm; and an ordered list of one or more transformer applications; storing the configuration file in a standard format in the one or more non-transitory computer-readable storage devices; instantiating a streamliner application configured to read the configuration file; accessing the configuration file, using the streamliner application, to identify the one or more attributes defining the machine learning algorithm and the ordered list of the one or more transformer applications; running, using the streamliner application, each transformer application of the one or more transformer applications in an order determined by the ordered list of the one or more transformer applications; creating an output file, which can comprise the machine learning algorithm as defined by the one or more attributes in the configuration file; storing the output file in a standard format in the one or more non-transitory computer-readable storage devices; using the output file to run one or more machine learning applications. 
     Various embodiments include a method. The method can include creating a configuration file which can comprise: one or more attributes defining a machine learning algorithm; and an ordered list of one or more transformer applications; storing the configuration file in a standard format in the one or more non-transitory computer-readable storage devices; instantiating a streamliner application configured to read the configuration file; accessing the configuration file, using the streamliner application, to identify the one or more attributes defining the machine learning algorithm and the ordered list of the one or more transformer applications; running, using the streamliner application, each transformer application of the one or more transformer applications in an order determined by the ordered list of the one or more transformer applications; creating an output file, which can comprise the machine learning algorithm as defined by the one or more attributes in the configuration file; storing the output file in a standard format in the one or more non-transitory computer-readable storage devices; using the output file to run one or more machine learning applications. 
     Turning to the drawings,  FIG. 1  illustrates an exemplary embodiment of a computer system  100 , all of which or a portion of which can be suitable for (i) implementing part or all of one or more embodiments of the techniques, methods, and systems and/or (ii) implementing and/or operating part or all of one or more embodiments of the memory storage modules described herein. As an example, a different or separate one of a chassis  102  (and its internal components) can be suitable for implementing part or all of one or more embodiments of the techniques, methods, and/or systems described herein. Furthermore, one or more elements of computer system  100  (e.g., a monitor  106 , a keyboard  104 , and/or a mouse  110 , etc.) also can be appropriate for implementing part or all of one or more embodiments of the techniques, methods, and/or systems described herein. Computer system  100  can comprise chassis  102  containing one or more circuit boards (not shown), a Universal Serial Bus (USB) port  112 , a Compact Disc Read-Only Memory (CD-ROM) and/or Digital Video Disc (DVD) drive  116 , and a hard drive  114 . A representative block diagram of the elements included on the circuit boards inside chassis  102  is shown in  FIG. 2 . A central processing unit (CPU)  210  in  FIG. 2  is coupled to a system bus  214  in  FIG. 2 . In various embodiments, the architecture of CPU  210  can be compliant with any of a variety of commercially distributed architecture families. 
     Continuing with  FIG. 2 , system bus  214  also is coupled to a memory storage unit  208 , where memory storage unit  208  can comprise (i) non-volatile memory, such as, for example, read only memory (ROM) and/or (ii) volatile memory, such as, for example, random access memory (RAM). The non-volatile memory can be removable and/or non-removable non-volatile memory. Meanwhile, RAM can include dynamic RAM (DRAM), static RAM (SRAM), etc. Further, ROM can include mask-programmed ROM, programmable ROM (PROM), one-time programmable ROM (OTP), erasable programmable read-only memory (EPROM), electrically erasable programmable ROM (EEPROM) (e.g., electrically alterable ROM (EAROM) and/or flash memory), etc. In these or other embodiments, memory storage unit  208  can comprise (i) non-transitory memory and/or (ii) transitory memory. 
     In various examples, portions of the memory storage module(s) of the various embodiments disclosed herein (e.g., portions of the non-volatile memory storage module(s)) can be encoded with a boot code sequence suitable for restoring computer system  100  ( FIG. 1 ) to a functional state after a system reset. In addition, portions of the memory storage module(s) of the various embodiments disclosed herein (e.g., portions of the non-volatile memory storage module(s)) can comprise microcode such as a Basic Input-Output System (BIOS) operable with computer system  100  ( FIG. 1 ). In the same or different examples, portions of the memory storage module(s) of the various embodiments disclosed herein (e.g., portions of the non-volatile memory storage module(s)) can comprise an operating system, which can be a software program that manages the hardware and software resources of a computer and/or a computer network. The BIOS can initialize and test components of computer system  100  ( FIG. 1 ) and load the operating system. Meanwhile, the operating system can perform basic tasks such as, for example, controlling and allocating memory, prioritizing the processing of instructions, controlling input and output devices, facilitating networking, and managing files. Exemplary operating systems can comprise one of the following: (i) Microsoft® Windows® operating system (OS) by Microsoft Corp. of Redmond, Wash., United States of America, (ii) Mac® OS X by Apple Inc. of Cupertino, Calif., United States of America, (iii) UNIX® OS, and (iv) Linux® OS. Further exemplary operating systems can comprise one of the following: (i) the iOS® operating system by Apple Inc. of Cupertino, Calif., United States of America, (ii) the Blackberry® operating system by Research In Motion (RIM) of Waterloo, Ontario, Canada, (iii) the WebOS operating system by LG Electronics of Seoul, South Korea, (iv) the Android™ operating system developed by Google, of Mountain View, Calif., United States of America, (v) the Windows Mobile™ operating system by Microsoft Corp. of Redmond, Wash., United States of America, or (vi) the Symbian™ operating system by Accenture PLC of Dublin, Ireland. 
     As used herein, “processor” and/or “processing module” means any type of computational circuit, such as but not limited to a microprocessor, a microcontroller, a controller, a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a graphics processor, a digital signal processor, or any other type of processor or processing circuit capable of performing the desired functions. In some examples, the one or more processing modules of the various embodiments disclosed herein can comprise CPU  210 . 
     Alternatively, or in addition to, the systems and procedures described herein can be implemented in hardware, or a combination of hardware, software, and/or firmware. For example, one or more application specific integrated circuits (ASICs) can be programmed to carry out one or more of the systems and procedures described herein. For example, one or more of the programs and/or executable program components described herein can be implemented in one or more ASICs. In many embodiments, an application specific integrated circuit (ASIC) can comprise one or more processors or microprocessors and/or memory blocks or memory storage. 
     In the depicted embodiment of  FIG. 2 , various I/O devices such as a disk controller  204 , a graphics adapter  224 , a video controller  202 , a keyboard adapter  226 , a mouse adapter  206 , a network adapter  220 , and other I/O devices  222  can be coupled to system bus  214 . Keyboard adapter  226  and mouse adapter  206  are coupled to keyboard  104  ( FIGS. 1-2 ) and mouse  110  ( FIGS. 1-2 ), respectively, of computer system  100  ( FIG. 1 ). While graphics adapter  224  and video controller  202  are indicated as distinct units in  FIG. 2 , video controller  202  can be integrated into graphics adapter  224 , or vice versa in other embodiments. Video controller  202  is suitable for monitor  106  ( FIGS. 1-2 ) to display images on a screen  108  ( FIG. 1 ) of computer system  100  ( FIG. 1 ). Disk controller  204  can control hard drive  114  ( FIGS. 1-2 ), USB port  112  ( FIGS. 1-2 ), and CD-ROM drive  116  ( FIGS. 1-2 ). In other embodiments, distinct units can be used to control each of these devices separately. 
     Network adapter  220  can be suitable to connect computer system  100  ( FIG. 1 ) to a computer network by wired communication (e.g., a wired network adapter) and/or wireless communication (e.g., a wireless network adapter). In some embodiments, network adapter  220  can be plugged or coupled to an expansion port (not shown) in computer system  100  ( FIG. 1 ). In other embodiments, network adapter  220  can be built into computer system  100  ( FIG. 1 ). For example, network adapter  220  can be built into computer system  100  ( FIG. 1 ) by being integrated into the motherboard chipset (not shown), or implemented via one or more dedicated communication chips (not shown), connected through a PCI (peripheral component interconnector) or a PCI express bus of computer system  100  ( FIG. 1 ) or USB port  112  ( FIG. 1 ). 
     Returning now to  FIG. 1 , although many other components of computer system  100  are not shown, such components and their interconnection are well known to those of ordinary skill in the art. Accordingly, further details concerning the construction and composition of computer system  100  and the circuit boards inside chassis  102  are not discussed herein. 
     Meanwhile, when computer system  100  is running, program instructions (e.g., computer instructions) stored on one or more of the memory storage module(s) of the various embodiments disclosed herein can be executed by CPU  210  ( FIG. 2 ). At least a portion of the program instructions, stored on these devices, can be suitable for carrying out at least part of the techniques and methods described herein. 
     Further, although computer system  100  is illustrated as a desktop computer in  FIG. 1 , there can be examples where computer system  100  may take a different form factor while still having functional elements similar to those described for computer system  100 . In some embodiments, computer system  100  may comprise a single computer, a single server, or a cluster or collection of computers or servers, or a cloud of computers or servers. Typically, a cluster or collection of servers can be used when the demand on computer system  100  exceeds the reasonable capability of a single server or computer. In certain embodiments, computer system  100  may comprise a portable computer, such as a laptop computer. In certain other embodiments, computer system  100  may comprise a mobile electronic device, such as a smartphone. In certain additional embodiments, computer system  100  may comprise an embedded system. 
     Turning ahead in the drawings,  FIG. 3  illustrates a block diagram of a system  300  that can be employed for a machine learning framework, as described in greater detail below. System  300  is merely exemplary and embodiments of the system are not limited to the embodiments presented herein. System  300  can be employed in many different embodiments or examples not specifically depicted or described herein. In some embodiments, certain elements or modules of system  300  can perform various procedures, processes, and/or activities. In these or other embodiments, the procedures, processes, and/or activities can be performed by other suitable elements or modules of system  300 . 
     Generally, therefore, system  300  can be implemented with hardware and/or software, as described herein. In some embodiments, part or all of the hardware and/or software can be conventional, while in these or other embodiments, part or all of the hardware and/or software can be customized (e.g., optimized) for implementing part or all of the functionality of system  300  described herein. 
     In some embodiments, system  300  can include a web server  310  and/or an Internet  320 . Web server  310  and/or internet  320  can each be a computer system, such as computer system  100  ( FIG. 1 ), as described above, and can each be a single computer, a single server, or a cluster or collection of computers or servers, or a cloud of computers or servers. In another embodiment, a single computer system can host each of two or more of web server  310  and/or internet  320 . Additional details regarding web server  310  and/or user internet  320  are described herein. 
     In many embodiments, system  300  also can comprise user computers  330 ,  331 . User computers  330 ,  331  can comprise any of the elements described in relation to computer system  100 . In some embodiments, user computers  330 ,  331  can be mobile devices. A mobile electronic device can refer to a portable electronic device (e.g., an electronic device easily conveyable by hand by a person of average size) with the capability to present audio and/or visual data (e.g., text, images, videos, music, etc.). For example, a mobile electronic device can comprise at least one of a digital media player, a cellular telephone (e.g., a smartphone), a personal digital assistant, a handheld digital computer device (e.g., a tablet personal computer device), a laptop computer device (e.g., a notebook computer device, a netbook computer device), a wearable user computer device, or another portable computer device with the capability to present audio and/or visual data (e.g., images, videos, music, etc.). Thus, in many examples, a mobile electronic device can comprise a volume and/or weight sufficiently small as to permit the mobile electronic device to be easily conveyable by hand. For examples, in some embodiments, a mobile electronic device can occupy a volume of less than or equal to approximately 1790 cubic centimeters, 2434 cubic centimeters, 2876 cubic centimeters, 4056 cubic centimeters, and/or 5752 cubic centimeters. Further, in these embodiments, a mobile electronic device can weigh less than or equal to 15.6 Newtons, 17.8 Newtons, 22.3 Newtons, 31.2 Newtons, and/or 44.5 Newtons. 
     Exemplary mobile electronic devices can comprise (i) an iPod®, iPhone®, iTouch®, iPad®, MacBook® or similar product by Apple Inc. of Cupertino, Calif., United States of America, (ii) a Blackberry® or similar product by Research in Motion (RIM) of Waterloo, Ontario, Canada, (iii) a Lumia® or similar product by the Nokia Corporation of Keilaniemi, Espoo, Finland, and/or (iv) a Galaxy™ or similar product by the Samsung Group of Samsung Town, Seoul, South Korea. Further, in the same or different embodiments, a mobile electronic device can comprise an electronic device configured to implement one or more of (i) the iPhone® operating system by Apple Inc. of Cupertino, Calif., United States of America, (ii) the Blackberry® operating system by Research In Motion (RIM) of Waterloo, Ontario, Canada, (iii) the Palm® operating system by Palm, Inc. of Sunnyvale, Calif., United States, (iv) the Android™ operating system developed by the Open Handset Alliance, (v) the Windows Mobile™ operating system by Microsoft Corp. of Redmond, Wash., United States of America, or (vi) the Symbian™ operating system by Nokia Corp. of Keilaniemi, Espoo, Finland. 
     Further still, the term “wearable user computer device” as used herein can refer to an electronic device with the capability to present audio and/or visual data (e.g., text, images, videos, music, etc.) that is configured to be worn by a user and/or mountable (e.g., fixed) on the user of the wearable user computer device (e.g., sometimes under or over clothing; and/or sometimes integrated with and/or as clothing and/or another accessory, such as, for example, a hat, eyeglasses, a wrist watch, shoes, etc.). In many examples, a wearable user computer device can comprise a mobile electronic device, and vice versa. However, a wearable user computer device does not necessarily comprise a mobile electronic device, and vice versa. 
     In specific examples, a wearable user computer device can comprise a head mountable wearable user computer device (e.g., one or more head mountable displays, one or more eyeglasses, one or more contact lenses, one or more retinal displays, etc.) or a limb mountable wearable user computer device (e.g., a smart watch). In these examples, a head mountable wearable user computer device can be mountable in close proximity to one or both eyes of a user of the head mountable wearable user computer device and/or vectored in alignment with a field of view of the user. 
     In more specific examples, a head mountable wearable user computer device can comprise (i) Google Glass™ product or a similar product by Google Inc. of Menlo Park, Calif., United States of America; (ii) the Eye Tap™ product, the Laser Eye Tap™ product, or a similar product by ePI Lab of Toronto, Ontario, Canada, and/or (iii) the Raptyr™ product, the STAR 1200™ product, the Vuzix Smart Glasses M100™ product, or a similar product by Vuzix Corporation of Rochester, N.Y., United States of America. In other specific examples, a head mountable wearable user computer device can comprise the Virtual Retinal Display™ product, or similar product by the University of Washington of Seattle, Wash., United States of America. Meanwhile, in further specific examples, a limb mountable wearable user computer device can comprise the iWatch™ product, or similar product by Apple Inc. of Cupertino, Calif., United States of America, the Galaxy Gear or similar product of Samsung Group of Samsung Town, Seoul, South Korea, the Moto 360 product or similar product of Motorola of Schaumburg, Ill., United States of America, and/or the Zip™ product, One™ product, Flex™ product, Charge™ product, Surge™ product, or similar product by Fitbit Inc. of San Francisco, Calif., United States of America. 
     In some embodiments, web server  310  can be in data communication through Internet  320  with user computers  330 ,  331 . In certain embodiments, user computers  330 ,  331  can be desktop computers, laptop computers, smart phones, tablet devices, and/or other endpoint devices. Web server  310  can host one or more websites. For example, web server  310  can host an eCommerce website that allows users to browse and/or search for products, to add products to an electronic shopping cart, and/or to purchase products, in addition to other suitable activities. 
     In many embodiments, web server  310 , Internet  320 , and/or user computers  330 ,  331  can each comprise one or more input devices (e.g., one or more keyboards, one or more keypads, one or more pointing devices such as a computer mouse or computer mice, one or more touchscreen displays, a microphone, etc.), and/or can each comprise one or more display devices (e.g., one or more monitors, one or more touch screen displays, projectors, etc.). In these or other embodiments, one or more of the input device(s) can be similar or identical to keyboard  104  ( FIG. 1 ) and/or a mouse  110  ( FIG. 1 ). Further, one or more of the display device(s) can be similar or identical to monitor  106  ( FIG. 1 ) and/or screen  108  ( FIG. 1 ). The input device(s) and the display device(s) can be coupled to the processing module(s) and/or the memory storage module(s) web server  310 , Internet  320 , and/or user computers  330 ,  331  in a wired manner and/or a wireless manner, and the coupling can be direct and/or indirect, as well as locally and/or remotely. As an example of an indirect manner (which may or may not also be a remote manner), a keyboard-video-mouse (KVM) switch can be used to couple the input device(s) and the display device(s) to the processing module(s) and/or the memory storage module(s). In some embodiments, the KVM switch also can be part of web server  310 , Internet  320 , and/or user computers  330 ,  331 . In a similar manner, the processing module(s) and the memory storage module(s) can be local and/or remote to each other. 
     In many embodiments, web server  310  can be configured to communicate with one or more user computers  330 ,  331 . In some embodiments, user computers  330 ,  331  also can be referred to as customer computers. In some embodiments, web server  310  can communicate or interface (e.g., interact) with one or more customer computers (such as user computers  330 ,  331 ) through a network or Internet  320 . Internet  320  can be an intranet that is not open to the public. Accordingly, in many embodiments, web server  310  (and/or the software used by such systems) can refer to a back end of system  300  operated by an operator and/or administrator of system  300 , and user computers  330 ,  331  (and/or the software used by such systems) can refer to a front end of system  300  used by one or more users  340 ,  341 , respectively. In some embodiments, users  340 ,  341  also can be referred to as customers, in which case, user computers  330 ,  331  can be referred to as customer computers. In these or other embodiments, the operator and/or administrator of system  300  can manage system  300 , the processing module(s) of system  300 , and/or the memory storage module(s) of system  300  using the input device(s) and/or display device(s) of system  300 . 
     Meanwhile, in many embodiments, web server  310  and/or user computers  330 ,  340  also can be configured to communicate with one or more databases. The one or more databases can comprise a product database that contains information about products, items, or SKUs (stock keeping units) sold by a retailer. The one or more databases can be stored on one or more memory storage modules (e.g., non-transitory memory storage module(s)), which can be similar or identical to the one or more memory storage module(s) (e.g., non-transitory memory storage module(s)) described above with respect to computer system  100  ( FIG. 1 ). Also, in some embodiments, for any particular database of the one or more databases, that particular database can be stored on a single memory storage module of the memory storage module(s), and/or the non-transitory memory storage module(s) storing the one or more databases or the contents of that particular database can be spread across multiple ones of the memory storage module(s) and/or non-transitory memory storage module(s) storing the one or more databases, depending on the size of the particular database and/or the storage capacity of the memory storage module(s) and/or non-transitory memory storage module(s). 
     The one or more databases can each comprise a structured (e.g., indexed) collection of data and can be managed by any suitable database management systems configured to define, create, query, organize, update, and manage database(s). Exemplary database management systems can include MySQL (Structured Query Language) Database, PostgreSQL Database, Microsoft SQL Server Database, Oracle Database, SAP (Systems, Applications, &amp; Products) Database, and IBM DB2 Database. 
     Meanwhile, communication between and/or within web server  310 , internet  320 , user computers  330 ,  331 , and/or the one or more databases can be implemented using any suitable manner of wired and/or wireless communication. Accordingly, system  300  can comprise any software and/or hardware components configured to implement the wired and/or wireless communication. Further, the wired and/or wireless communication can be implemented using any one or any combination of wired and/or wireless communication network topologies (e.g., ring, line, tree, bus, mesh, star, daisy chain, hybrid, etc.) and/or protocols (e.g., personal area network (PAN) protocol(s), local area network (LAN) protocol(s), wide area network (WAN) protocol(s), cellular network protocol(s), powerline network protocol(s), etc.). Exemplary PAN protocol(s) can comprise Bluetooth, Zigbee, Wireless Universal Serial Bus (USB), Z-Wave, etc.; exemplary LAN and/or WAN protocol(s) can comprise Institute of Electrical and Electronic Engineers (IEEE) 802.3 (also known as Ethernet), IEEE 802.11 (also known as WiFi), etc.; and exemplary wireless cellular network protocol(s) can comprise Global System for Mobile Communications (GSM), General Packet Radio Service (GPRS), Code Division Multiple Access (CDMA), Evolution-Data Optimized (EV-DO), Enhanced Data Rates for GSM Evolution (EDGE), Universal Mobile Telecommunications System (UMTS), Digital Enhanced Cordless Telecommunications (DECT), Digital AMPS (IS-136/Time Division Multiple Access (TDMA)), Integrated Digital Enhanced Network (iDEN), Evolved High-Speed Packet Access (HSPA+), Long-Term Evolution (LTE), WiMAX, etc. The specific communication software and/or hardware implemented can depend on the network topologies and/or protocols implemented, and vice versa. In many embodiments, exemplary communication hardware can comprise wired communication hardware including, for example, one or more data buses, such as, for example, universal serial bus(es), one or more networking cables, such as, for example, coaxial cable(s), optical fiber cable(s), and/or twisted pair cable(s), any other suitable data cable, etc. Further exemplary communication hardware can comprise wireless communication hardware including, for example, one or more radio transceivers, one or more infrared transceivers, etc. Additional exemplary communication hardware can comprise one or more networking components (e.g., modulator-demodulator components, gateway components, etc.). 
     In many embodiments, the techniques described herein can provide a practical application and several technological improvements. In some embodiments, the techniques described herein can provide for easier and faster development of machine learning algorithms, as described in greater detail below. These techniques described herein can provide a significant improvement over conventional approaches of manually developing and managing machine learning models. In many embodiments, the techniques described herein can beneficially automatically develop machine learning algorithms based on dynamic information that describes current conditions and/or conditions that have occurred during a same day of a machine learning model being rolled out. 
     In many embodiments, the techniques described herein can be used continuously at a scale that cannot be handled using manual techniques. For example, a number of features managed by a machine learning framework can exceed a few thousand. 
     In a number of embodiments, the techniques described herein can solve a technical problem that arises only within the realm of computer networks, as online orders do not exist outside the realm of computer networks. Moreover, the techniques described herein can solve a technical problem that cannot be solved outside the context of computer networks. Specifically, the techniques described herein cannot be used outside the context of computer networks, in view of a lack of data, and because the machine learning model cannot be created without a computer. 
     Turning ahead in the drawings,  FIG. 4  illustrates a block diagram of a web server  310  that can be employed for a machine learning framework, as described in greater detail below. System  400  is merely exemplary and embodiments of the system are not limited to the embodiments presented herein. System  400  can be employed in many different embodiments or examples not specifically depicted or described herein. In some embodiments, certain elements or modules of system  400  can perform various procedures, processes, and/or activities. In these or other embodiments, the procedures, processes, and/or activities can be performed by other suitable elements or modules of system  400 . 
     Generally, therefore, system  400  and/or individual elements of system  400  can be implemented with hardware and/or software, as described herein. In some embodiments, part or all of the hardware and/or software can be conventional, while in these or other embodiments, part or all of the hardware and/or software can be customized (e.g., optimized) for implementing part or all of the functionality of system  400  described herein. 
     In many embodiments, system  400  can comprise an extraction, transform load (“ETL”) system  401 . In various embodiments, ELT system  401  can be a hardware and/or software module configured to perform ETL tasks on or within a database. For example, an ETL system can perform extraction tasks, transformation tasks, and/or load tasks. In some embodiments, an extraction task can comprise reading data from one or more databases. In various embodiments, a transform task can comprise converting extracted data into one or more different formats. The extracted data can be in many formats that are different from each other, and the ETL system can convert or transform those formats such that the output from the ETL system can be in a standard format or multiple standard formats. In the same or different embodiments, converting extracted data into one or more different formats can comprise using a lookup table, combining database (e.g., performing a join), and/or performing other operations on the data to convert it from one format to another. In various embodiments, a load task can comprise writing data into a database. In the same or different embodiments, an output of ETL system  401  can comprise raw feature data. In the same or different embodiments, raw feature data can comprise transactional data, production information data, interactional data, demographic data, and/or identity mapping data. IN many embodiments, an ETL layer can output an ETL output file comprising a text file, a sequence file, and/or a parquet file. In many embodiments, as noted above, raw feature data can be stored in a standard format. In some embodiments, a standard format can comprise a text file, a sequence file, and/or a parquet file, as described above. In various embodiments, an ETL system  401  can be configured to create a user identity mapping. In the same or different embodiments, a user identity mapping can comprise an identification of a specific user using online actions of a user, offline actions of a user, and/or demographics of a user. In many embodiments, online actions of a user can comprise online transactions, online marketing engagement e.g., opening an email or reading an electronic message), website interactions etc. In the same or different embodiments, offline interactions of a user can comprise in store purchases, real world marketing engagement (e.g., using a coupon sent through the postal service or handed out in store), location data, etc. In many embodiments, online actions of a user, offline actions of a user, and/or demographics of a user can be linked together using an identifier. In some embodiments, an identifier can comprise a phone number, an email address, payment information (e.g., credit card number, debit count number, etc.), an account name, and/or a unique identifier of an electronic device (e.g., IP address, MAC ID, SSAID, etc.). 
     In many embodiments, system  400  can comprise a feature engineering system  402 . In the same or different embodiments, feature engineering system  402  can be a hardware and/or software module configured to convert raw feature data from ETL system  401  into a format that can be used by a model building system  403 . In the same or different embodiments, feature engineering system  402  can be configured to perform method  700  ( FIG. 7 ) as described in further detail below. 
     In many embodiments, system  400  can comprise a model building system  403 . In the same or different embodiments, model building system  403  can be a hardware and/or software module configured to take an output from feature engineering system  402 , and convert the output into a machine learning model, which is output from model building system  403 . In the same or different embodiments, model building system  403  can perform method  800  ( FIG. 8 ) as described in further detail below. 
     In many embodiments, system  400  can comprise a machine learning application  404 . In the same or different embodiments, a machine learning application  404  can be a hardware and/or software module configured to run a machine learning model created by model building system  403 . In various embodiments, a machine learning model can be used to perform a variety of activities related to predicting probabilities of future activities and/or actions. For example, a machine learning model can be used to understand natural human language, provide recommendations, aid in machine vision, identify handwriting, diagnose disease, etc. 
     In many embodiments, system  400  can comprise a monitoring system  405 . In the same or different embodiments, monitoring system  405  can be a hardware and/or software module configured to monitor machine learning models built by model building system  403  and run by machine learning application  404 . In various embodiments, monitoring system  405  can monitor real time performance of machine learning models built by model building system  403  and run by machine learning application  404 , and provide feedback to system  400 . This feedback can then be used to improve machine learning models built by model building system  403  and run by machine learning application  404 . Monitoring system  405  also can receive input from ETL system  401  and feature engineering system  402 , to be used in determining the feedback for system  400 . In some embodiments, monitoring system  405  can visualize information comrpsing a distribution of predicted values and/or a number of instances with a certain predicted value. In the same or different embodiments, monitoring system  405  can be configured to report evaluation metrics, evaluate facts versus predictions, and other useful information and/or visualizations describing a performance of system  400 . 
     Turning ahead in the drawings,  FIG. 5  illustrates a block diagram of a system  500  that can be employed for a machine learning framework, as described in greater detail below. System  500  is merely exemplary and embodiments of the system are not limited to the embodiments presented herein. System  500  can be employed in many different embodiments or examples not specifically depicted or described herein. In some embodiments, certain elements or modules of system  500  can perform various procedures, processes, and/or activities. In various embodiments, system  500  can perform method  700  ( FIG. 7 ) as described in greater detail below. In these or other embodiments, the procedures, processes, and/or activities can be performed by other suitable elements or modules of system  500 . 
     Generally, therefore, system  500  and/or individual elements of system  500  can be implemented with hardware and/or software, as described herein. In some embodiments, part or all of the hardware and/or software can be conventional, while in these or other embodiments, part or all of the hardware and/or software can be customized (e.g., optimized) for implementing part or all of the functionality of system  500  described herein. 
     In many embodiments, system  500  can comprise a configuration file  503 , feature engineering system  402 , and an output file  505 . Feature engineering system  402  can comprise a reading application  501  (also known as (AKA) a reader application). Generally speaking, a reading application  501  can comprise a hardware and/or software module configured to read and interpret an output of ETL system  401  ( FIG. 4 ). In many embodiments, reading application  501  can be configured to access and read data stored and/or produced by an ETL system  401  ( FIG. 4 ). In the same or different embodiments, reading application  501  can be configured to access data stored and/or produced by ETL system  401  by querying its file path (e.g., hdfs path). In many embodiments, querying a file path can comprise using a getPath method. In the same or different embodiments, a getPath method can comprise two different signatures. In embodiments where a getPath method comprises two different signatures, developers of reading application  501  can pass a set of input parameters to reading application  501 , which can output a corresponding path for instance classes that inherit from the input parameters. In many embodiments, a first signature of a getPath method can transmit a date and/or epoch, a number of consequent days and/or epochs, a step (e.g., whether to move back or forward), and can return a set of paths in a form of a Unix path expression (e.g. {/path/to/data/date=20180101, /path/to/data/date=20180102, /path/to/data/date=20180103}). A second signature of a getPath method can transmit a start date and/or epoch, an end date and/or epoch, a step as described above, and can return a set of paths in a form of a Unix path expression. In various embodiments, a getPath method can append prefixes and/or suffixes to paths, as specified by a developer in a specific instance. 
     In many embodiments, querying a file path can comprise using a getDefaultPipeByConf method. In the same or different embodiments, a getDefaultPipeByConf method can transmit a configuration object from configuration file  503 , and return a pipe (e.g, a data frame) with specified data. In many embodiments, a configuration object can comprise a start date and/or epoch, a duration, a direction, and/or a partition parameter. In the same or different embodiments, a configuration object can define specified data. In various embodiments, a getDefaultPipeByConf can use a getPath method, as described above, to find underlying paths, read the data from the underlying paths, and then loads the data from the underlying paths into a pipe. In many embodiments, when getDefaultPipeByConf reads content of underlying files from ETL layer  401 , it can be configured to split each line according to a specified delimiter in an instance, and convert data to an appropriate data type according to data schema. 
     In some embodiments, reading application  501  can be configured to read different formats of outputs of ETL system  401  (e.g. text, sequence, and/or compressed files). In many embodiments, a reading application  501  can be configured to interpret one or more data types stored and/or produced by ETL system  401  by analyzing a schema of the data (e.g., by being able to analyze fields and their data types), using a delimiter used to separate fields, and/or analyzing partition information when data is partitioned. 
     In many embodiments, a reading application  501  can be configured to be customizable and transferrable between development of different machine learning models. In other words, a base/default reading application can be modified by a developer depending on a specific machine learning model that will be developed. In many embodiments, modifications to a reading application can comprises specifying a path to desired data, altering a delimiter used to separate fields, altering a partition field, and/or altering a schema of data. This modified reading application can then be saved on one or more non-transitory storage devices, and then be reused to create a similar machine learning model and/or a machine learning model that uses data found by a reading application. In this way, using a reading application can lessen coding time of a developer of a machine learning algorithm by removing the need to create a bespoke or customized reading application. 
     In many embodiments, feature engineering system  402  also can comprise a unifying application  502  (AKA a unifier application). Generally speaking, a unifying application  502  can be a hardware and/or software module configured to implement business logic required for building features. In many embodiments, a unifying application  502  can comprise a module in charge of implementing semantics of a feature. For example, when a developer wants to capture a user&#39;s transactions at a division level, ETL application  401  can provide a source for transactions of a user and a hierarchy/taxonomy of products. In this example, a reader application  501  can be instantiated to read transactional data, and another reader application  501  can be instantiated to read product hierarchy information. Here, a unifying application can be used to generate a user&#39;s transactions at a division level by joining transactional data and product hierarchy data on an item level, and then aggregating the results at division level. 
     In many embodiments, a unifying application  502  can be configured to perform a specific type of join (e.g., left, right, full, etc.) that should be used when joining different instances of a same unifier or joining different unifiers. In many embodiments, a unifying application  502  can be configured to specify a join field within a same unifier (e.g., a field that is used to join features collected from different time periods of a same unifier) or a join field to be used in joining different unifiers. In the same or different embodiments, a unifier can be configured to use a generateUnifiedPipe method. In various embodiments, a generateUnifiedPipe method can comprise taking a configuration object (e.g., start date, duration, direction, etc.) and return a pipe. In many embodiments, in order to avoid ambiguous column names, a developer of a unifying application  502  can add a name of a specific unifier concatenated with a name of a period of time from a configuration file  503 , and use it as a column name. In many embodiments, a unifier can use an internal key to identify records and an external key to join with other unifying applications  502 . Generally speaking, an internal key and/or an external key can function to aid in identifying records in data frames. For example, if a key is a user id, and a reading application reads one month of data, then the data can be aggregated at user id level using the key. In this way, there can be one record per user in the data frame, and each record can show an aggregated version of the user&#39;s activities (e.g. number of orders) over a one month period. 
     In many embodiments, a unifying application  502  can be configured to be customizable and transferrable between development of different machine learning models. In other words, a base/default unifying application can be modified by a developer depending on a specific machine learning model that will be developed. In many embodiments, modifications to a unifying application can comprise specifying an internal key, specifying an external keys, specifying a type of join (e.g., inner, left outer, full outer, etc.) to be used when joining unifying applications, logic to be implemented by a unifying application (e.g., a set of data transformation). This modified unifying application can then be saved on one or more non-transitory storage devices, and then be reused to create a similar machine learning model and/or a machine learning model that uses a same business logic. In this way, using a unifying application can lessen coding time of a developer of a machine learning algorithm by removing the need to create a bespoke or customized reading application. 
     In many embodiments, as noted above, system  500  can comprise configuration file  503  (AKA a config file). Generally speaking, a configuration file can comprise a hardware and/or software module configured to identify features needed in a machine learning algorithm and a time period from which the features will be collected. In many embodiments, a time period can comprise 1 day, 2 days, 3 days, 4 days, 5 days, 10 days, 20 days, 1 month, 2 months, 3 months, 4 months, 5 months, 6 months, 9 months, 1 year, 2 years, 3 years, 4 years, 5 years, etc. In many embodiments, a configuration file  503  can comprise a list of unifying applications that are needed for generation of a specific machine learning algorithm. In the same or different embodiments, a configuration file can comprise a list of time periods from which features should be collected. For example, a configuration file can be configured to instruct a unifying application to collect transactions at a division level for each customer, and then break them into periods of 3 months for the preceding 12 months. In various embodiments, a configuration file can comprise a number of distinct fields where unifying applications, features, and/or time periods can be specified. For example,  FIG. 11  displays an exemplary embodiment of a configuration file. As can be seen in  FIG. 11 , a unifier named “EmailRecipientUnifier” is listed, which is configured to collect a number of emails received by users. As can be seen in  FIG. 11 , this embodiment of a unifier states that statistics for each user will be collected over two time periods. One time period comprises a 6 day period (represented by “1”) and another time period comprises an 89 day period (represented by “quarter1”). 
     Returning to  FIG. 5 , in many embodiments, a configuration file  503  can be configured to be customizable and transferrable between development of different machine learning models. In other words, a base/default unifying application can be modified by a developer depending on a specific machine learning model that will be developed. In many embodiments, modifications to a configuration file can comprise modifying one or more fields of the configuration file as described further within this document. This modified configuration file can then be saved on one or more non-transitory storage devices, and then be reused to create a similar machine learning model and/or a machine learning model that uses a same set of unifiers and feature data. In this way, using a configuration file can lessen coding time of a developer of a machine learning algorithm by removing the need to create a bespoke or customized set of specifications for creation of a machine learning model. 
     Returning now to  FIG. 5 , in many embodiments, feature engineering system  402  also can comprise an orchestrating application  504  (AKA an orchestrator). Generally speaking, an orchestrating application  504  can comprise a hardware and/or software module configured to instantiate, access, and/or run other elements of system  500 . In many embodiments, an orchestrating application can be configured read a configuration file and generate features based on parameters written within the configuration file by an administrator. In the same or different embodiments, an orchestrating application can read a configuration file and create a list of tuples. In many embodiments, each tuple of a list of tuples can describe a Unifier and/or a list of config objects (e.g. start date, duration, and name). In some embodiments, an orchestrating application can be configured to read an identity mapping provided by ETL later 401, and use this identity mapping to create features. In various embodiments, an orchestrating application can be configured to group unifying applications of a same group into categories based on an identity field. In the same or different embodiments, an orchestrating application can be configured to join unifying applications across different groups using an identity mapping provided by ETL later 401. 
     As an example, in one embodiment there can be three unifying applications in a configuration file: one unifying application determining a number of emails users receive, a second unifying application determining a number of emails opened by the users, and a third unifying application determining division level transactions made by users. In this example, the first unifying application and the second unifying application can use an email address as an attribute value in an identity field, and the third unifying application can use a user id as an attribute value in an identity field. Here, the orchestrating application can place the first unifying application and the second unifying application in a same group based on the same attribute value in the respective identity fields, and the third unifying application in a different group. The orchestrating application can then join the first unifying application and the second unifying application at the email address field to create a combination unifying application, and can join this combination unifying application with the third unifying application using the identity mapping (e.g., by mapping user ids in the third unifying application to the corresponding email addresses using the identity mapping information). 
     In many embodiments, an orchestrating application can be configured to instantiate appropriate unifying applications based upon a list of unifying applications in a configuration file. In the same or different embodiments, an orchestrating application loads unifying applications using Reflection, a programming technique to create, modify, and invoke methods on objects at run time. 
     In many embodiments, as noted above, system  500  can comprise output file  505 . In the same or different embodiments, an output file can comprise a hardware and/or software module created by feature engineering system  402  ( FIG. 4 ) and configured to be read by a model building system  403  ( FIG. 4 ). In various embodiments, an output file can comprise feature data. In many embodiments, feature data in an output file can be stored in a standardized format. In some embodiments, feature data in an output file can be atomic (e.g., feature data comprising a single value) or can comprise a list of values. In embodiments where feature data comprises a list of values, the feature data can be encapsulated into a conceptual feature, thereby keeping a number of output features smaller. In many embodiments, an output file can be stored in a standard format, and can be stored as a sparse representation for list features. Storage efficiency can be improved by encapsulating feature vectors into coarser, conceptual feature vectors by utilizing sparse representation. As an example, instead of having a plurality of feature vectors representing a number of orders in different departments for a user, these feature vectors can be grouped into one conceptual feature vector that represents orders in the different departments. In some embodiments, a sparse representation of a feature vector can store only non-zero counts for features in the feature vector. Therefore, continuing with the above referenced example, when a user makes purchases only in a small number of departments rather than a large number of departments, many counts in a conceptual feature can be zero, and therefore not stored in the sparse representation of the feature vector. 
     As another example, in one embodiment, a marketplace comprises 170 departments at a retailer, and customers&#39; transactions are collected at monthly level. It is unlikely that a customer shops across all 170 departments in the same month. On the contrary, an average customer may shop in only one of two of these departments on a monthly basis. If sparse representation is not used, in order to store the number of transactions a customer makes in a given month, 170 values need to be stored, most of which are zero and only a few of which are greater than zero. The sparse representation makes it possible to store the non-zero values only, instead of storing everything (including all of the zeros) to reduce significantly the size of the output. Using a sparse representation, then, can reduce required storage space, and can consequently make subsequent reading and/or processing of the sparse representation of the feature vector faster than reading and/or processing of one or more feature vectors that are zeros. In many embodiments, a sparse representation of a feature vector can be stored in a database as described above. Further, storing an output file using a sparse representation can minimize a file size of the output file, a processing time required to read the output file, and a portability of the output file. This sparse representation can be in a standard format. 
     Turning ahead in the drawings,  FIG. 6  illustrates a block diagram of a system  600  that can be employed for a machine learning framework, as described in greater detail below. System  600  is merely exemplary and embodiments of the system are not limited to the embodiments presented herein. System  600  can be employed in many different embodiments or examples not specifically depicted or described herein. In some embodiments, certain elements or modules of system  600  can perform various procedures, processes, and/or activities. In various embodiments, system  600  can perform method  800  ( FIG. 7 ) as described in greater detail below. In these or other embodiments, the procedures, processes, and/or activities can be performed by other suitable elements or modules of system  600 . 
     Generally, therefore, system  600  and/or individual elements of system  600  can be implemented with hardware and/or software, as described herein. In some embodiments, part or all of the hardware and/or software can be conventional, while in these or other embodiments, part or all of the hardware and/or software can be customized (e.g., optimized) for implementing part or all of the functionality of system  600  described herein. 
     In many embodiments, system  600  can comprise a configuration file  602 , model building system  403 , and an output file  604 . Model building system  403  can comprise a transforming application  601  (AKA a transformer). Generally speaking, a transformer application  601  can comprise a hardware and/or software module configured to transform feature data produced by feature engineering system  402 . In the same or different embodiments, a transformer application can be configured to filter feature data, standardize features, sample, build models, scoring users, and/or evaluating models. In many embodiments, filtering feature data can comprise filtering by a number of orders made on an eCommerce website. In various embodiments, standardizing features can comprise subtracting a mean value of a feature from a specific value of a feature, and divide a standard deviation of a feature to make a range of different features closer to each other. In some embodiments, sampling can comprise keeping one or more training instances (e.g., a user&#39;s feature values) with a label of one and down sampling training instances with a label of 0 to make a training set balanced (i.e. make a ratio of training instance with a label 1 and 0 the same or similar). In many embodiments, building models can comprise training a logistic regression model on a training set to translate a user feature vector into a probability score that represents a probability of opening a message by that user. In the same or different embodiments, scoring users can comprise providing a user&#39;s feature vector as an input argument to a logistic regression model, and getting a probability of opening a message by the user as an output. In many embodiments, evaluating models can comprise using a model to calculate a probability of opening a message by a user, sending the user a message, waiting for a certain period of time, collecting data, checking whether the user has opened the message or not, comparing this factual data against a the prediction and determining how accurately the model was able to predict the user&#39;s actions (in this case opening a message). 
     In many embodiments, a transforming application can be configured to take feature data as input and convert it to another data format. In some embodiments, transforming applications can be re-used across development of different machine learning algorithms and/or stored in a library of transformer applications, thereby making development of iterative machine learning algorithms faster, as compared with the prior art, while requiring minimal coding time by a developer. Use of transformer applications can also makes a code base for a machine learning framework concise and easier to maintain. 
     In many embodiments, as noted above, system  600  can comprise configuration file  602  (AKA a config file). In various embodiments, a configuration  602  can be a same or different configuration file than configuration file  503  ( FIG. 5 ). Generally speaking, a configuration file  602  can comprise a hardware and/or software module configured to allow an administrator and/or developer to specify settings such as a schema of feature data and/or transformations that can be applied to the feature data. In various embodiments, a configuration file can comprise one or more fields, which are described in further detail below. In the same or different embodiments, alteration of one or more fields of a configuration file can change a machine learning model developed by system  600 . An exemplary configuration file is shown in  FIG. 12 , which displays the one or more fields and values of those fields. 
     Returning to  FIG. 6 , in some embodiments, a configuration file  602  can be configured to identify training schema. In many embodiments, training schema can comprise specifying a schema of feature data used to build and/or train a machine learning model. For training example, a schema can comprise a user id, a number of emails received between 60 to 30 days ago, a number of emails between over 60 to 30 days ago, a number of emails clicked between 60 to 30 days ago, a number of days since a user has opted in to receive messages, age of a user, gender of a user, education level of a user, a field representing whether the user has opened a marketing message in the last 30 days, etc. 
     In many embodiments, a configuration file  602  can be configured to identify testing schema. In some embodiments, testing schema can comprise specifying a schema of feature data used for scoring purposes. For example, a testing schema can comprise a user id, a number of emails received between 60 to 30 days ago, a number of emails between over 60 to 30 days ago, a number of emails clicked between 60 to 30 days ago, a number of days since a user has opted in to receive messages, age of a user, gender of a user, education level of a user, a field representing whether the user has opened a marketing message in the last 30 days, etc. 
     In various embodiments, a configuration file  602  can be configured to identify a feature schema. In many embodiments, a feature schema can comprise a subset of a training schema and/or a testing schema. For example, a number of emails received between 60 to 30 days ago, a number of emails opened between 60 to 30 days ago, and/or a number of emails clicked over 60 to 30 days ago 
     In the same or different embodiments, a configuration file  602  can be configured to identify a key. In various embodiments, a key can comprise a subset of a training schema and/or a testing schema. For example, a key can be a user ID. 
     In some embodiments, a configuration file  602  can be configured to identify a label. In many embodiments, a label can comprise a subset of a training schema and/or a testing schema. In various embodiments, a label can comprise a feature of training schema and/or testing schema that is to be predicted by a machine learning model. For example, a field representing whether the user has opened a message in the last 30 days. 
     In many embodiments, a configuration file  602  can be configured to identify a synthetic feature. In some embodiments, a synthetic feature can comprise a feature that can be generated as a function of existing features. For example, when training data comprises a number of received emails and a number of opened emails, a synthetic feature of open rate can be determined by dividing a number of opened emails by a number of received emails. 
     In various embodiments, a configuration file  602  can be configured to identify a categorical feature, In many embodiments, a categorical feature can comprise a subset of a feature and/or a synthetic feature. In some embodiments, a categorical feature can comprise a feature whose value can be categorical. For example, a gender of a user and/or an education level of a user. 
     In the same or different embodiments, a configuration file  602  can be configured to identify a model type. In various embodiments, a model type can comprise an identification of a type of model to be built by system  600 . In many embodiments, an identification of a type of model to be built can comprise an identification of model hyper parameters. In many embodiments, hyper parameters can comprise parameters with values that are set before training a model, as opposed to other parameter values that can be derived via the training process. For example, hyper parameters can comprise a learning rate in a logistic regression algorithm and/or a number of trees in a random forest algorithm. 
     In some embodiments, a configuration file  602  can be configured to identify a transformer flow. In the same or different embodiments, a transformer flow can comprise an ordered list of transforming applications. In various embodiments, an ordered list of transforming applications can determine an order in which transforming applications are instantiated by a streamlining application, as described in further detail below. For example, in embodiments where a machine learning model is built to predict a message open rate for a user, a transformer flow can comprise a transformer to sample data, standardize input features, and build a model. 
     When compared with past methods of developing a machine learning model, using a configuration file can make this process faster and more convenient for a developer. Adding and/or removing new features and using a resulting set of features to build models can be as simple as modifying a “features” field in a configuration file, as opposed to spending hours developing bespoke, customized applications to do the same thing. This improvement, then, can enable a developer to try different models comprising different tunings with very little effort (e.g., by merely modifying fields in a configuration file). Further, configuration files can be reusable and, therefore, can be easily applied to multiple different projects. For example, in an embodiment where training data comprises features of a number emails received, a number emails opened, and a number emails clicked, the same configuration file can be used to build a first machine learning model predicting an open rate and a second machine learning model predicting a click rate. A developer can simply alter a “label” field in the first configuration file from open rate to click rate to produce the second machine learning model. 
     In many embodiments, model building system  403  also can comprise a streamlining application  603  (AKA a streamliner). Generally speaking, a streamlining application can comprise a hardware and/or software module configured to read a configuration file  602 , instantiate transforming applications, and then execute the transforming applications according to an order listed in a transformer flow. In various embodiments, a streamlining application can be configured to extract keys, features, and labels from input data (e.g. a configuration file  603  and/or an output file  505  ( FIG. 5 )), as well as to generate synthetic features when they synthetic features are described in a configuration file  602 . In some embodiments, a streamlining application  603  can be similar to an orchestrating application  504  ( FIG. 5 ) in that a streamlining application can enable a developer to decouple building blocks of a machine learning algorithm from its development. In this way, code sharing can be encouraged and facilitated by system  600 . In the same or different embodiments, a streamlining application can use a reflection method, as described above, to instantiate transformer applications. In various embodiments, a streamlining application can instantiate transformer applications in an order specified in a transformer flow of a configuration file. Therefore, in these embodiments, data can be passed from one transformer application to a next transformer application in the order specified in the transformer flow of the configuration file. In other words, in some embodiments, an output of one transformer application can be an input to the next transformer application. 
     In many embodiments, as noted above, system  600  can comprise output file  604 . Generally speaking, output file  604  can comprise a hardware and/or software module configured to be run on a machine learning application  404 . In many embodiments, an output file  604  can be created by a transformer application. In the same or different embodiments, an output file  604  can be created by a last transformer application in a transformer flow. 
     Turning ahead in the drawings,  FIG. 7  illustrates a flow chart for a method  700 , according to an embodiment. Method  700  is merely exemplary and is not limited to the embodiments presented herein. Method  700  can be employed in many different embodiments or examples not specifically depicted or described herein. In some embodiments, the activities of method  700  can be performed in the order presented. In other embodiments, the activities of method  700  can be performed in any suitable order. In still other embodiments, one or more of the activities of method  700  can be combined or skipped. In many embodiments, system  300  ( FIG. 3 ) can be suitable to perform method  700  and/or one or more of the activities of method  700 . In these or other embodiments, one or more of the activities of method  700  can be implemented as one or more computer instructions configured to run at one or more processing modules and configured to be stored at one or more non-transitory memory storage modules. Such non-transitory memory storage modules can be part of a computer system such as system  100  ( FIG. 1 ). The processing module(s) can be similar or identical to the processing module(s) described above with respect to computer system  100  ( FIG. 1 ). 
     In many embodiments, method  700  can comprise an activity  701  of receiving a matrix from an ETL application. In various embodiments, the ETL application can comprise ETL system  401  ( FIG. 4 ). In some embodiments, activity  701  can be performed by system  500  ( FIG. 5 ). In more particular embodiments, activity  701  can be performed by reading application  501  ( FIG. 5 ) and/or an orchestrating application  504  ( FIG. 5 ). In the same or different embodiments, a matrix can comprise raw feature data as produced by ETL system  401  ( FIG. 4 ). 
     Next, in many embodiments, method  700  can comprise an activity  702  of storing a matrix in one or more non-transitory storage devices. In some embodiments, activity  702  can be performed by system  500  ( FIG. 5 ). In more particular embodiments, activity  702  can be performed by orchestrating application  504  ( FIG. 5 ). In the same or different embodiments, a matrix can comprise raw feature data stored in a standard format, as described above with reference to ETL system  401 . 
     Then, in many embodiments, method  700  can continue by comprising an activity  703  of receiving a configuration file. In some embodiments, activity  703  can be performed by system  500  ( FIG. 5 ). In more particular embodiments, activity  703  can be performed by orchestrating application  504  ( FIG. 5 ). In various embodiments, a configuration file can comprise configuration file  503  as described above. In some embodiments, a configuration can be received over a computer network, such as system  300  ( FIG. 3 ). 
     In many embodiments, method  700  can continue further by comprising an activity  704  of storing a configuration file. In some embodiments, activity  704  can be performed by system  500  ( FIG. 5 ). In more particular embodiments, activity  704  can be performed by orchestrating application  504  ( FIG. 5 ). In many embodiments, a configuration file can be stored on one or more non-transitory storage devices as described with regards to system  100  ( FIG. 1 ). In some embodiments, a configuration file can be stored in a standard format, such as a format described with reference to configuration file  503  and/or shown in  FIG. 11 . 
     Next, in many embodiments, method  700  can comprise an activity  705  of instantiating one or more unifier applications. In some embodiments, activity  705  can be performed by system  500  ( FIG. 5 ). In more particular embodiments, activity  705  can be performed by orchestrating application  504  ( FIG. 5 ). In the same or different embodiments, one or more unifier applications can be instantiated based upon a configuration file, as described with reference to configuration file  503  ( FIG. 5 ). 
     After activity  705 , in many embodiments, method  700  can optionally comprise an activity  706  of identifying a list of unifier applications. In some embodiments, activity  706  can be performed by system  500  ( FIG. 5 ). In more particular embodiments, activity  706  can be performed by orchestrating application  504  ( FIG. 5 ). 
     Also after activity  705 , in many embodiments, method  700  can optionally comprise an activity  707  of receiving an identity mapping from an ETL application. In some embodiments, activity  707  can be performed by system  500  ( FIG. 5 ). In more particular embodiments, activity  707  can be performed by unifying application  502  ( FIG. 5 ) and/or an orchestrating application  504  ( FIG. 5 ). In many embodiments, an ETL application can comprise ETL system  401  ( FIG. 4 ), and an identity can be the same or different than an identity mapping described with reference to ETL system  401  ( FIG. 4 ). 
     In many embodiments, method  700  can continue after activity  707  by comprising an activity  708  of joining one or more unifier applications. In some embodiments, activity  708  can be performed by system  500  ( FIG. 5 ). In more particular embodiments, activity  708  can be performed by orchestrating application  504  ( FIG. 5 ). In some embodiments, an identity mapping can be used to join one or more unifier applications, as described above. In many embodiments, joining one or more unifier applications can identify a user, as described above. 
     In many embodiments, method  700  also can comprise an activity  709  of identifying relevant feature data. In some embodiments, activity  709  can be performed by system  500  ( FIG. 5 ). In more particular embodiments, activity  709  can be performed by reading application  501  ( FIG. 5 ), unifier application  502  ( FIG. 5 ), and/or orchestrating application  504  ( FIG. 5 ). In many embodiments, relevant feature data can comprise a portion of data received from ETL system  401 . Activity  709  can occur after activity  705 , and also can occur after activity  706  (if performed) and/or after activities  707  and  708  (if performed). 
     After activity  709 , in many embodiments, method  700  can comprise an optional activity  710  of accessing a configuration file. In some embodiments, activity  710  can be performed by system  500  ( FIG. 5 ). In more particular embodiments, activity  710  can be performed by reading application  501  ( FIG. 5 ), unifier application  502  ( FIG. 5 ), and/or orchestrating application  504  ( FIG. 5 ). In many embodiments, a configuration file can comprise configuration file  503 . In the same or different embodiments, a configuration file can be accessed to identify a start date, a direction, and/or a duration. In various embodiments, a start date, a direction, and/or a duration can be the same or different than a start date, a direction, and/or a duration described with reference to configuration file  503 . 
     In many embodiments, method  700  can continue after activity  710  by comprising an activity  711  of filtering a matrix. In some embodiments, activity  711  can be performed by system  500  ( FIG. 5 ). In more particular embodiments, activity  711  can be performed by reading application  501  ( FIG. 5 ), unifier application  502  ( FIG. 5 ), and/or orchestrating application  504  ( FIG. 5 ). In various embodiments, a matrix can be filtered based upon a start date, a direction, and/or a duration such that only entries in the matrix comprising the start date, the direction, and/or the duration remain in the matrix. In mane embodiments, a start date, a direction, and/or a duration can be the same or different than a start date, a direction, and/or a duration described with reference to configuration file  503 . 
     Also after activity  709 , in many embodiments, method  700  can comprise an optional activity  712  of instantiating one or more reading applications. In various embodiments, one or more reading applications can comprise reading application  501 . In some embodiments, activity  712  can be performed by system  500  ( FIG. 5 ). In more particular embodiments, activity  712  can be performed by orchestrating application  504  ( FIG. 5 ). In the same or different embodiments, one or more reading applications can be instantiated based upon a configuration file, as described with reference to configuration file  503  ( FIG. 5 ). 
     In many embodiments, method  700  can continue after activity  712  by comprising an activity  713  of identifying a first data. In some embodiments, activity  713  can be performed by system  500  ( FIG. 5 ). In more particular embodiments, activity  713  can be performed by orchestrating application  504  ( FIG. 5 ). In the same or different embodiments, a first data can comprise a subset of raw feature data as provided by ETL system  401  ( FIG. 4 ). In various embodiments, first data can comprise a first type of raw feature data, as described with reference to reading application  501 . 
     In many embodiments, method  700  can continue after activity  703  by comprising an activity  714  of discarding second data. In some embodiments, activity  714  can be performed by system  500  ( FIG. 5 ). In more particular embodiments, activity  714  can be performed by orchestrating application  504  ( FIG. 5 ). In various embodiments, second data can comprise a portion of the raw feature data not comprising first data. 
     In many embodiments, method  700  also can comprise an activity  715  of storing relevant feature data. In some embodiments, activity  715  can be performed by system  500  ( FIG. 5 ). In more particular embodiments, activity  715  can be performed by orchestrating application  504  ( FIG. 5 ). In various embodiments, relevant feature data can be stored in an output file. In the same or different embodiments, an output file can comprise output file  505 . In many embodiments, relevant feature data can be stored in a standardized format, as described with reference to output file  505 . Activity  715  can occur after activity  709 , and also can occur after activities  710  and  711  (if performed) and/or after activities  712 ,  713 , and  714  (if performed). 
     In many embodiments, activity  705  can further comprise modifying a configuration file. In some embodiments, a configuration file can comprise configuration file  503 , and modifying it can comprise altering, deleting, changing, and/or adding a new field in the configuration file. In various embodiments, a reading application  501 , a unifying application  502 , and/or an orchestrating application  504  can be modified instead of or in addition to a configuration file. In these embodiments, modifying a reading application  501 , a unifying application  502 , and/or an orchestrating application  504  can comprise altering, deleting, changing, and/or adding one or more fields in the streamlining application and/or transforming application. 
     In many embodiments, activity  705  can further comprise using a second configuration file to create a second machine learning algorithm. In some embodiments, a second reading application, a second unifying application, and/or a second orchestrating application can be used instead of or in addition to a second configuration file. In various embodiments, creating a second machine learning algorithm can comprise using method  700  with a second configuration file, a second reading application, a second unifying application, and/or a second orchestrating application. 
     After activity  715 , in many embodiments, method  700  can comprise an activity  716  of transmitting an output file. In some embodiments, activity  716  can be performed by system  500  ( FIG. 5 ). In more particular embodiments, activity  716  can be performed by orchestrating application  504  ( FIG. 5 ). In many embodiments, an output file can be the same or different than output file  505 . In the same or different embodiments, an output file can be transmitted over a computer network in real time so that a model building system can have up to date information. In many embodiments, a computer network can comprise system  300  ( FIG. 3 ), as described above. In some embodiments, a model building system can comprise model building system  403 . 
     Turning ahead in the drawings,  FIG. 8  illustrates a flow chart for a method  800 , according to an embodiment. Method  800  is merely exemplary and is not limited to the embodiments presented herein. Method  700  can be employed in many different embodiments or examples not specifically depicted or described herein. In some embodiments, the activities of method  800  can be performed in the order presented. In other embodiments, the activities of method  800  can be performed in any suitable order. In still other embodiments, one or more of the activities of method  800  can be combined or skipped. In many embodiments, system  300  ( FIG. 3 ) can be suitable to perform method  800  and/or one or more of the activities of method  800 . In these or other embodiments, one or more of the activities of method  800  can be implemented as one or more computer instructions configured to run at one or more processing modules and configured to be stored at one or more non-transitory memory storage modules. Such non-transitory memory storage modules can be part of a computer system such as system  100  ( FIG. 1 ). The processing module(s) can be similar or identical to the processing module(s) described above with respect to computer system  100  ( FIG. 1 ). 
     In many embodiments, method  800  can comprise an activity  801  creating a configuration file. In various embodiments, a configuration file can comprise configuration file  602  ( FIG. 6 ) and/or the text of a configuration file shown in  FIG. 12 . Returning to  FIG. 8 , in some embodiments, activity  801  can be performed by system  600  ( FIG. 6 ) or an administrator of system  600  ( FIG. 6 ). In the same or different embodiments, a configuration file can comprise one or more attributes defining a machine learning algorithm and/or an ordered list of one or more transformer applications, as described with reference to configuration file  602  ( FIG. 6 ). In various embodiments, a transformer application can comprise transforming application  601  ( FIG. 6 ). In many embodiments, one or more attributes defining a machine learning algorithm can comprise training schema, testing schema, model parameters, at least one feature of the training schema, at least one feature of the testing schema, a key of the training schema, a key of the testing schema, a label of the training schema at least one new feature created using the at least one feature of the training schema, at least one new feature created using the at least one feature of the testing schema, at least one categorical feature of the at least one feature of the training schema, and/or at least one categorical feature of the at least one feature of the testing schema, a label of the testing schema as described with reference to configuration file  602  ( FIG. 6 ). 
     In many embodiments, method  800  can continue by comprising an activity  802  of storing a configuration file in a standard format. In the same or different embodiments, a configuration file can comprise configuration file  602  ( FIG. 6 ) and/or the text of a configuration file shown in  FIG. 12 . Returning to  FIG. 8 , in the same or different embodiments, a standard format can comprise a format of a configuration file as described with reference to configuration file  602  ( FIG. 6 ) and shown in  FIG. 12 . 
     Returning to  FIG. 8 , in many embodiments, method  800  can continue by comprising an activity  803  of instantiating a streamliner application. In some embodiments, activity  803  can be performed by system  600  ( FIG. 6 ) or an administrator of system  600  ( FIG. 6 ). In the same or different embodiments, a streamliner application can comprise streamliner application  603  ( FIG. 6 ). The streamline application can be configured to read the configuration file. 
     Then, in many embodiments, method  800  can comprise an activity  804  of accessing a configuration file. In various embodiments, activity  804  can be performed by streamlining application  603  ( FIG. 6 ). In the same or different embodiments, a configuration file can comprise the configuration file as described with reference to configuration file  602  ( FIG. 6 ) and/or the text of a configuration file shown in  FIG. 12 . 
     Returning to  FIG. 8 , in many embodiments, method  800  can continue by comprising an activity  805  of running each transformer application. In the same or different embodiments, activity  805  can be performed by streamlining application  603  ( FIG. 6 ). In various embodiments, each transformer application can comprise a separate one of transforming application  601  ( FIG. 6 ). In many embodiments, a transformer application can comprise a filtering transformer application, a standardizing transformer application, a sampling transformer application, a model building transformer application, a scoring transformer application, and/or a model evaluating transformer application. 
     After activity  805 , in many embodiments, method  800  can optionally comprise an activity  806  of querying a library of transformer applications. In some embodiments, activity  806  can be performed by streamlining application  603  ( FIG. 6 ). In the same or different embodiments, a transformer application can comprise transforming application  601  ( FIG. 6 ) as descried above. 
     In many embodiments, method  800  can continue after activity  806  by optionally comprising instantiating transformer application  807 . In some embodiments, activity  807  can be performed by streamlining application  603  ( FIG. 6 ). In the same or different embodiments, a transformer application can comprise transforming application  601  ( FIG. 6 ) as descried above. 
     In many embodiments, method  800  also can comprise creating an output file  808 . In some embodiments, activity  808  can be performed by transforming application  601  ( FIG. 6 ) and/or streamlining application  603  ( FIG. 6 ). In the same or different embodiments, an output file can comprise output file  604  ( FIG. 6 ) as described above. Activity  808  can occur after activity  805 , and also can occur after activities  806  and  807  (if performed). 
     In many embodiments, method  800  can comprise an activity  809  of modifying a configuration file. In some embodiments, a configuration file can comprise configuration file  602 , and modifying it can comprise altering, deleting, changing, and/or adding a new field in the configuration file. In various embodiments, a streamlining application and/or a transforming application can be modified instead of or in addition to a configuration file. In these embodiments, modifying a transforming application and/or a streamlining application can comprise altering, deleting, changing, and/or adding one or more fields in the streamlining application and/or transforming application. 
     In many embodiments, method  800  can comprise an activity  810  of using a second configuration file to create a second machine learning algorithm. In some embodiments, a second streamlining application and/or a transforming application can be used instead of or in addition to a second configuration file. In various embodiments, creating a second machine learning algorithm can comprise using method  800  with a second configuration file, second streamlining application, and/or a second transforming application. 
     Turning ahead in the drawings,  FIG. 9  illustrates a block diagram of a system  900  that can be employed for behavior based messaging. System  900  is merely exemplary and embodiments of the system are not limited to the embodiments presented herein. System  900  can be employed in many different embodiments or examples not specifically depicted or described herein. In some embodiments, certain elements or modules of system  700  can perform various procedures, processes, and/or activities. In these or other embodiments, the procedures, processes, and/or activities can be performed by other suitable elements or modules of system  900 . 
     Generally, therefore, system  900  can be implemented with hardware and/or software, as described herein. In some embodiments, part or all of the hardware and/or software can be conventional, while in these or other embodiments, part or all of the hardware and/or software can be customized (e.g., optimized) for implementing part or all of the functionality of system  900  described herein. 
     In many embodiments, system  900  can comprise non-transitory memory storage module  901 . Memory storage module  901  can be referred to as matrix receiving module  901 . In many embodiments, matrix receiving module  901  can store computing instructions configured to run on one or more processing modules and perform one or more acts of method  700  ( FIG. 7 ) (e.g., activity  701  ( FIG. 7 )). 
     In many embodiments, system  900  can comprise non-transitory memory storage module  902 . Memory storage module  902  can be referred to as matrix storing module  902 . In many embodiments, matrix storing module  902  can store computing instructions configured to run on one or more processing modules and perform one or more acts of method  700  ( FIG. 7 ) (e.g., activity  702  ( FIG. 7 )). 
     In many embodiments, system  900  can comprise non-transitory memory storage module  903 . Memory storage module  903  can be referred to as configuration file receiving module  903 . In many embodiments, configuration file receiving module  903  can store computing instructions configured to run on one or more processing modules and perform one or more acts of method  700  ( FIG. 7 ) (e.g., activity  703  ( FIG. 7 )). 
     In many embodiments, system  900  can comprise non-transitory memory storage module  904 . Memory storage module  904  can be referred to as configuration file storing module  904 . In many embodiments, configuration file storing module  904  can store computing instructions configured to run on one or more processing modules and perform one or more acts of method  700  ( FIG. 7 ) (e.g., activity  704  ( FIG. 7 )). 
     In many embodiments, system  900  can comprise non-transitory memory storage module  905 . Memory storage module  905  can be referred to as unifier application instantiating module  905 . In many embodiments, unifier application instantiating module  905  can store computing instructions configured to run on one or more processing modules and perform one or more acts of method  700  ( FIG. 7 ) (e.g., activity  705  ( FIG. 7 )). 
     In many embodiments, system  900  can comprise non-transitory memory storage module  906 . Memory storage module  906  can be referred to as unifier identifying module  906 . In many embodiments, unifier identifying module  906  can store computing instructions configured to run on one or more processing modules and perform one or more acts of method  700  ( FIG. 7 ) (e.g., activity  706  ( FIG. 7 )). 
     In many embodiments, system  900  can comprise non-transitory memory storage module  907 . Memory storage module  907  can be referred to as identity mapping receiving module  907 . In many embodiments, identity mapping receiving module  907  can store computing instructions configured to run on one or more processing modules and perform one or more acts of method  700  ( FIG. 7 ) (e.g., activity  707  ( FIG. 7 )). 
     In many embodiments, system  900  can comprise non-transitory memory storage module  908 . Memory storage module  908  can be referred to as unifier joining module  908 . In many embodiments, unifier joining module  908  can store computing instructions configured to run on one or more processing modules and perform one or more acts of method  700  ( FIG. 7 ) (e.g., activity  708  ( FIG. 7 )). 
     In many embodiments, system  900  can comprise non-transitory memory storage module  909 . Memory storage module  909  can be referred to as relevant feature identifying module  909 . In many embodiments, relevant feature identifying module  909  can store computing instructions configured to run on one or more processing modules and perform one or more acts of method  700  ( FIG. 7 ) (e.g., activity  709  ( FIG. 7 )). 
     In many embodiments, system  900  can comprise non-transitory memory storage module  910 . Memory storage module  910  can be referred to as configuration file accessing module  910 . In many embodiments, configuration file accessing module  910  can store computing instructions configured to run on one or more processing modules and perform one or more acts of method  700  ( FIG. 7 ) (e.g., activity  710  ( FIG. 7 )). 
     In many embodiments, system  900  can comprise non-transitory memory storage module  911 . Memory storage module  911  can be referred to as matrix filtering module  911 . In many embodiments, matrix filtering module  911  can store computing instructions configured to run on one or more processing modules and perform one or more acts of method  700  ( FIG. 7 ) (e.g., activity  711  ( FIG. 7 )). 
     In many embodiments, system  900  can comprise non-transitory memory storage module  912 . Memory storage module  912  can be referred to as reading application instantiating module  912 . In many embodiments, reading application instantiating module  912  can store computing instructions configured to run on one or more processing modules and perform one or more acts of method  700  ( FIG. 7 ) (e.g., activity  712  ( FIG. 7 )). 
     In many embodiments, system  900  can comprise non-transitory memory storage module  913 . Memory storage module  913  can be referred to as first data identifying module  913 . In many embodiments, first data identifying module  913  can store computing instructions configured to run on one or more processing modules and perform one or more acts of method  700  ( FIG. 7 ) (e.g., activity  713  ( FIG. 7 )). 
     In many embodiments, system  900  can comprise non-transitory memory storage module  914 . Memory storage module  914  can be referred to as second data discarding module  914 . In many embodiments, second data discarding module  914  can store computing instructions configured to run on one or more processing modules and perform one or more acts of method  700  ( FIG. 7 ) (e.g., activity  714  ( FIG. 7 )). 
     In many embodiments, system  900  can comprise non-transitory memory storage module  915 . Memory storage module  915  can be referred to as relevant feature storing module  915 . In many embodiments, relevant feature storing module  915  can store computing instructions configured to run on one or more processing modules and perform one or more acts of method  700  ( FIG. 7 ) (e.g., activity  715  ( FIG. 7 )). 
     In many embodiments, system  900  can comprise non-transitory memory storage module  916 . Memory storage module  916  can be referred to as output file transmitting module  916 . In many embodiments, output file transmitting module  916  can store computing instructions configured to run on one or more processing modules and perform one or more acts of method  700  ( FIG. 7 ) (e.g., activity  716  ( FIG. 7 )). 
     Turning ahead in the drawings,  FIG. 10  illustrates a block diagram of a system  1000  that can be employed for behavior based messaging. System  1000  is merely exemplary and embodiments of the system are not limited to the embodiments presented herein. System  1000  can be employed in many different embodiments or examples not specifically depicted or described herein. In some embodiments, certain elements or modules of system  700  can perform various procedures, processes, and/or activities. In these or other embodiments, the procedures, processes, and/or activities can be performed by other suitable elements or modules of system  1000 . 
     Generally, therefore, system  1000  can be implemented with hardware and/or software, as described herein. In some embodiments, part or all of the hardware and/or software can be conventional, while in these or other embodiments, part or all of the hardware and/or software can be customized (e.g., optimized) for implementing part or all of the functionality of system  1000  described herein. 
     In many embodiments, system  1000  can comprise non-transitory memory storage module  1001 . Memory storage module  1001  can be referred to as configuration file creating module  1001 . In many embodiments, configuration file creating module  1001  can store computing instructions configured to run on one or more processing modules and perform one or more acts of method  800  ( FIG. 8 ) (e.g., activity  801  ( FIG. 8 )). 
     In many embodiments, system  1000  can comprise non-transitory memory storage module  1002 . Memory storage module  1002  can be referred to as configuration file storing module  1002 . In many embodiments, configuration file storing module  1002  can store computing instructions configured to run on one or more processing modules and perform one or more acts of method  800  ( FIG. 8 ) (e.g., activity  802  ( FIG. 8 )). 
     In many embodiments, system  1000  can comprise non-transitory memory storage module  1003 . Memory storage module  1003  can be referred to as streamliner instantiating module  1003 . In many embodiments, streamliner instantiating module  1003  can store computing instructions configured to run on one or more processing modules and perform one or more acts of method  800  ( FIG. 8 ) (e.g., activity  803  ( FIG. 8 )). 
     In many embodiments, system  1000  can comprise non-transitory memory storage module  1004 . Memory storage module  1004  can be referred to as configuration file accessing module  1004 . In many embodiments, configuration file accessing module  1004  can store computing instructions configured to run on one or more processing modules and perform one or more acts of method  800  ( FIG. 8 ) (e.g., activity  804  ( FIG. 8 )). 
     In many embodiments, system  1000  can comprise non-transitory memory storage module  1005 . Memory storage module  1005  can be referred to as transformer running module  1005 . In many embodiments, transformer running module  1005  can store computing instructions configured to run on one or more processing modules and perform one or more acts of method  800  ( FIG. 8 ) (e.g., activity  805  ( FIG. 8 )). 
     In many embodiments, system  1000  can comprise non-transitory memory storage module  1006 . Memory storage module  1006  can be referred to as library querying module  1006 . In many embodiments, library querying module  1006  can store computing instructions configured to run on one or more processing modules and perform one or more acts of method  800  ( FIG. 8 ) (e.g., activity  806  ( FIG. 8 )). 
     In many embodiments, system  1000  can comprise non-transitory memory storage module  1007 . Memory storage module  1007  can be referred to as transformer instantiating module  1007 . In many embodiments, transformer instantiating module  1007  can store computing instructions configured to run on one or more processing modules and perform one or more acts of method  800  ( FIG. 8 ) (e.g., activity  807  ( FIG. 8 )). 
     In many embodiments, system  1000  can comprise non-transitory memory storage module  1008 . Memory storage module  1008  can be referred to as output file creating module  1008 . In many embodiments, output file creating module  1008  can store computing instructions configured to run on one or more processing modules and perform one or more acts of method  800  ( FIG. 8 ) (e.g., activity  808  ( FIG. 8 )). 
     Although systems and methods for a machine learning framework have been described with reference to specific embodiments, it will be understood by those skilled in the art that various changes may be made without departing from the spirit or scope of the disclosure. Accordingly, the disclosure of embodiments is intended to be illustrative of the scope of the disclosure and is not intended to be limiting. It is intended that the scope of the disclosure shall be limited only to the extent required by the appended claims. For example, to one of ordinary skill in the art, it will be readily apparent that any element of  FIGS. 1-12  may be modified, and that the foregoing discussion of certain of these embodiments does not necessarily represent a complete description of all possible embodiments. For example, one or more of the procedures, processes, or activities of  FIG. 7  may include different procedures, processes, and/or activities and be performed by many different modules, in many different orders. 
     All elements claimed in any particular claim are essential to the embodiment claimed in that particular claim. Consequently, replacement of one or more claimed elements constitutes reconstruction and not repair. Additionally, benefits, other advantages, and solutions to problems have been described with regard to specific embodiments. The benefits, advantages, solutions to problems, and any element or elements that may cause any benefit, advantage, or solution to occur or become more pronounced, however, are not to be construed as critical, required, or essential features or elements of any or all of the claims, unless such benefits, advantages, solutions, or elements are stated in such claim. 
     Moreover, embodiments and limitations disclosed herein are not dedicated to the public under the doctrine of dedication if the embodiments and/or limitations: (1) are not expressly claimed in the claims; and (2) are or are potentially equivalents of express elements and/or limitations in the claims under the doctrine of equivalents.