Patent Publication Number: US-2023161565-A1

Title: Machine learning development hub

Description:
TECHNICAL FIELD 
     The subject application generally relates to software development, and more particularly, to tools for developing artificial intelligence and machine learning systems. 
     BACKGROUND 
     Data scientists tasked with developing machine learning systems have a large number of different tools and platforms available to them. Examples tools include those offered by Domino Data Labs such as Domino Platform and Domino Compute. Amazon Web Services (AWS) also provides a variety of tools, including Amazon S3, Amazon&#39;s API Gateway, AWS Lambda, Amazon Kinesis Data streams, and Amazon Kinesis Data Firehose. Microsoft offers the Microsoft Azure Machine Learning (ML) platform. Kubeflow offers the Kubeflow Pipeline. 
     The proliferation of available tools provides options for machine learning development, however, it also leads to problems. Data scientists are presented with deciding on the best tools for various tasks such as fetching data, building models, and running inferences on top of pre-built models. To use all the available tools, data scientists must develop expertise in containers, Kubernetes, data security, endpoints, scaling, persistent volumes, graphical processing units (GPUs), DevOps, programming in various languages, use of various tools, etc. This leads to a slower development process, even for relatively simple tasks such as testing simple customer use cases. 
     Vendor fragmentation and enterprise silos have created an unnecessarily complex infrastructure. This complex infrastructure delays the time to onboard legacy and new software. Also, for many available tools, leveraging innovative open source is unfortunately not possible, given vendor specific dependencies. 
     The above-described background is merely intended to provide a contextual overview of some current issues and is not intended to be exhaustive. Other contextual information may become further apparent upon review of the following detailed description. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The technology described herein is illustrated by way of example and not limited in the accompanying figures in which like reference numerals indicate similar elements and in which: 
         FIG.  1    illustrates an example machine learning development hub, in accordance with one or more embodiments described herein. 
         FIG.  2    illustrates an architectural overview including example technologies that can interact to provide a machine learning development hub, in accordance with one or more embodiments described herein. 
         FIG.  3    illustrates an example software development kit (SDK) layer of a machine learning development hub, in accordance with one or more embodiments described herein. 
         FIG.  4    illustrates example provisioning of artifacts to a machine learning development hub user interface, in accordance with one or more embodiments described herein. 
         FIG.  5    illustrates example mediation of communications between microservices, in accordance with one or more embodiments described herein. 
         FIG.  6    illustrates example security functions of a machine learning development hub, in accordance with one or more embodiments described herein. 
         FIG.  7    is a flow diagram of an example, non-limiting computer implemented method that can be performed by a machine learning development hub, in accordance with one or more embodiments described herein. 
         FIG.  8    is a flow diagram of another example, non-limiting computer implemented method that can be performed by a machine learning development hub, in accordance with one or more embodiments described herein. 
         FIG.  9    is a flow diagram of another example, non-limiting computer implemented method that can be performed by a machine learning development hub, in accordance with one or more embodiments described herein. 
         FIG.  10    illustrates a block diagram of an example computer operable to provide any of the various devices described herein. 
     
    
    
     DETAILED DESCRIPTION 
     One or more embodiments are now described with reference to the drawings, wherein like reference numerals are used to refer to like elements throughout. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the various embodiments. It may be evident, however, that the various embodiments can be practiced without these specific details, e.g., without applying to any particular networked environment or standard. In other instances, well-known structures and devices are shown in block diagram form in order to facilitate describing the embodiments in additional detail. 
     Example embodiments are directed to a machine learning development hub, and corresponding methods and computer readable media. The machine learning development hub can comprise a machine learning development platform complete with various tools for various stages of machine learning development. The machine learning development hub can furthermore comprise translation functions to translate received inputs into inputs to other machine learning development platforms. The machine learning development hub can collect credentials for the other machine learning development platforms and can connect to the other machine learning development platforms via their respective interfaces, in order to supply inputs and instructions thereto. The machine learning development hub can encrypt its communications to other machine learning development platforms to secure its interactions. Further aspects and embodiments of this disclosure are described in detail below. 
     As used in this disclosure, in some embodiments, the terms “component,” “system” and the like are intended to refer to, or comprise, a computer-related entity or an entity related to an operational apparatus with one or more specific functionalities, wherein the entity can be either hardware, a combination of hardware and software, software, or software in execution. As an example, a component can be, but is not limited to being, a process running on a processor, a processor, an object, an executable, a thread of execution, computer-executable instructions, a program, and/or a computer. By way of illustration and not limitation, both an application running on a server and the server can be a component. 
     One or more components can reside within a process and/or thread of execution and a component can be localized on one computer and/or distributed between two or more computers. In addition, these components can execute from various computer readable media having various data structures stored thereon. The components can communicate via local and/or remote processes such as in accordance with a signal having one or more data packets (e.g., data from one component interacting with another component in a local system, distributed system, and/or across a network such as the internet with other systems via the signal). As another example, a component can be an apparatus with specific functionality provided by mechanical parts operated by electric or electronic circuitry, which is operated by a software application or firmware application executed by a processor, wherein the processor can be internal or external to the apparatus and executes at least a part of the software or firmware application. As yet another example, a component can be an apparatus that provides specific functionality through electronic components without mechanical parts, the electronic components can comprise a processor therein to execute software or firmware that confers at least in part the functionality of the electronic components. While various components have been illustrated as separate components, it will be appreciated that multiple components can be implemented as a single component, or a single component can be implemented as multiple components, without departing from example embodiments. 
     The term “facilitate” as used herein is in the context of a system, device or component “facilitating” one or more actions or operations, in respect of the nature of complex computing environments in which multiple components and/or multiple devices can be involved in some computing operations. Non-limiting examples of actions that may or may not involve multiple components and/or multiple devices comprise transmitting or receiving data, establishing a connection between devices, determining intermediate results toward obtaining a result, etc. In this regard, a computing device or component can facilitate an operation by playing any part in accomplishing the operation. When operations of a component are described herein, it is thus to be understood that where the operations are described as facilitated by the component, the operations can be optionally completed with the cooperation of one or more other computing devices or components, such as, but not limited to, sensors, antennae, audio and/or visual output devices, other devices, etc. 
     Further, the various embodiments can be implemented as a method, apparatus or article of manufacture using standard programming and/or engineering techniques to produce software, firmware, hardware or any combination thereof to control a computer to implement the disclosed subject matter. The term “article of manufacture” as used herein is intended to encompass a computer program accessible from any computer-readable (or machine-readable) device or computer-readable (or machine-readable) storage/communications media. For example, computer readable storage media can comprise, but are not limited to, magnetic storage devices (e.g., hard disk, floppy disk, magnetic strips), optical disks (e.g., compact disk (CD), digital versatile disk (DVD)), smart cards, and flash memory devices (e.g., card, stick, key drive). Of course, those skilled in the art will recognize many modifications can be made to this configuration without departing from the scope or spirit of the various embodiments. 
       FIG.  1    illustrates an example machine learning development hub, in accordance with one or more embodiments described herein. The example machine learning (ML) development hub  100  includes a platform layer  110  and a translation layer  120 . The platform layer  110  includes example tools  111 ,  112 ,  113  and a selector  114 . The translation layer  120  includes example translators  121 ,  122 ,  123  and security  130 .  FIG.  1    furthermore comprises example infrastructure platforms  141 ,  142 ,  143 , wherein infrastructure platform  143  comprises example tool  153 . 
     In example operations according to  FIG.  3   , the tools  111 ,  112 ,  113  in the platform layer  110  can be provided to a user via a user interface. The user can interact with tools  111 ,  112 ,  113 , and the user interactions and/or data provided via the tools  111 ,  112 ,  113  can be received as inputs  101 ,  102 , and  103 . The translation layer  120  can translate the inputs  101 ,  102 , and  103  into translated inputs  101 A,  102 A,  102 B, and  103 A, and provide the translated inputs  101 A,  102 A,  102 B, and  103 A to infrastructure platforms  141 ,  142 , and  143 . The translation layer  120  can be configured to access Application Programming Interfaces (APIs) for each of the infrastructure platforms  141 ,  142 , and  143 , and the translation layer  120  can supply the translated inputs  101 A,  102 A,  102 B, and  103 A to the infrastructure platforms  141 ,  142 , and  143  via applicable APIs. 
     The infrastructure platforms  141 ,  142 , and  143  can include any platforms available for machine learning development, whether such platforms are now in use or developed in the future. For example, the infrastructure platforms  141 ,  142 , and  143  can include the platforms discussed in the background section, such as Domino Platform, Domino Compute, Amazon S3, Amazon&#39;s API Gateway, AWS Lambda, Amazon Kinesis Data streams, Amazon Kinesis Data Firehose, Microsoft Azure ML platform, Kubeflow Pipeline, and any other such ML development tools and platforms. The translation layer  120  can include translators  121 ,  122 ,  123  that are configured to translate inputs  101 ,  102 ,  103  into translated inputs  101 A,  102 A,  102 B, and  103 A that are structured and formatted for use by the target infrastructure platforms  141 ,  142 , and  143 . Security  130  can use stored credentials, such as stored usernames and passwords, to gain access to the infrastructure platforms  141 ,  142 , and  143 , so that translated inputs  101 A,  102 A,  102 B, and  103 A can be supplied in connection with appropriate user accounts. Security  130  can also be configured to encrypt transmissions of translated inputs  101 A,  102 A,  102 B, and  103 A as described herein. 
     In some embodiments, the selector  114  can be configured to receive user selections of target infrastructure platforms  141 ,  142 , and  143  for the tools  111 ,  112 ,  113 . For example, the infrastructure platform  141  can be selected as a target for tool  111 . The infrastructure platforms  141  and  142  can be selected as targets for tool  112 . The infrastructure platform  143 , and more specifically, the tool  153  can be selected as a target for tool  113 . The selector  114  can then configure the platform layer  110  and the translation layer  120  to apply appropriate translators  121 ,  122 ,  123  in connection with translating inputs  101 ,  102 ,  103  received by tools  111 ,  112 ,  113 . The selector  114  can also be configured to receive and store user credentials for each of the infrastructure platforms  141 ,  142 , and  143 , for use by security  130 . 
     In general, with reference to  FIG.  1   , the ML development hub  100  can be configured to interact with multiple different infrastructure platforms  141 ,  142 , and  143  on behalf of users, so that users can interact with the ML development hub  100 , and the ML development hub  100  can handle the details associated with interactions with the infrastructure platforms  141 ,  142 , and  143  on the user&#39;s behalf. In some embodiments, the ML development hub  100  can be implemented at least in part by a software development kit (SDK) as described herein. The SDK can allow teams to create and update translation technologies, such as translators  121 ,  122 ,  123 , to work with different tools  111 ,  112 ,  113  and infrastructure platforms  141 ,  142 , and  143 . While not illustrated in  FIG.  1   , the platform layer  110  can also optionally include freestanding tools which can operate exclusively within the platform layer  110 , without supplying translated inputs to infrastructure platforms  141 ,  142 , and  143 . 
     Data practitioners at large-scale enterprises realize that gathering intelligence from data is an integral part of analytics operations. Some of the advantages that can be achieved by embodiments of this disclosure include efficient access to compute, storage, open source and data access; the ability to rapidly bootstrap re-usable ML components with best practices that meet an organization&#39;s security, governance and compliance requirements; the ability to rapidly on-board legacy and new software; the provision of high-level APIs that pre-configure infrastructure resources with defaults and expedite building and deployment of applications without need for specialized platform knowledge; the ability to access many proprietary technology silos while maintaining environment controls that ensure data privacy and security; and the acceleration of development processes through enhanced familiarity with features for tasks such as launching loops, conditions, objects and statements to run production ML workloads. 
     Some example inputs  101 ,  102 ,  103  can comprise, e.g., compute inputs that can specify computing resources to be used for ML models. Other example inputs  101 ,  102 ,  103  can comprise storage inputs that specify storage locations, formats, and other parameters for storing data used by ML models. Other example inputs  101 ,  102 ,  103  can comprise data inputs that specify data to be used by ML models as well as any data parameters or other requirements. Other example inputs  101 ,  102 ,  103  can comprise open source inputs that specify open source to be used in connection with ML models, and data access inputs that specify data access for ML models. 
       FIG.  2    illustrates an architectural overview including example technologies that can interact to provide a machine learning development hub, in accordance with one or more embodiments described herein. The platform layer  200  and translation layer  205  illustrated in  FIG.  2    can implement a platform layer  110  and translation layer  120 , resepectively, such as introduced in  FIG.  1   . 
     The example platform layer  200  includes various tools, data, frameworks, libraries and platforms, which are referred to generally as tools  111 ,  112 ,  113  in  FIG.  1   . For example, platform layer  200  includes AI workspace tools  210 , AI analytic tools  220 , AI data  230 , deep learning frameworks  240 , ML libraries  250 , and ML platforms  260 . Example AI workspace tools  210  include Jupyter Notebook  211 , Rstudio  212 , Rshiny  213 , and VSCode  214 . Example AI analytic tools  220  can include Python  221 , R  222 , and Microsoft Cognitive Services  223 . Example AI data  230  can include Neo4J  231 , Postgres DB  232 , and GreenPlum  233 . Example deep learning frameworks  240  can include Tensor flow  241 , MXNet  242 , and Pytorch  243 . Example ML libraries  250  can include Spark Mlib  251 , Sckkit-learn  252 , and XgBoost  253 . Example ML platforms  260  can include Airflow  261 , MLFlow  262  and Kubeflow  263 . These various tools illustrated in the platform layer  200  are familiar to data practitioners, however the aggregation of these tools on a platform that can translate inputs to the tools and provide translated inputs to other platforms as needed, with appropriate security, is a novel aspect of the architecture illustrated in  FIG.  2   . 
     The example translation layer  205  includes an SDK  271 , a container as a service (CaaS) layer  272 , and a technology cloud  273 . The operation of the SDK  271  is described further in connection with the example SDK illustrated in  FIG.  4   . Translated inputs produced by the SDK  271  with support of the CaaS layer  272  and technology cloud  273  can be provided to access and compute infrastructure  280 . The access and compute infrastructure  280  can include, e.g., infrastructure platforms  141 ,  142 , and  143  illustrated in  FIG.  1   . In  FIG.  2   , the access and compute infrastructure  280  is illustrated as including direct resources  281 , on premises resources  282 , and cloud resources  283 . The division of access and compute infrastructure  280  into the example categories  281 ,  282 , and  283  demonstrates that an ML development hub  100  can cooperate with various types of infrastructure, whether such infrastructure is directly available (direct  281 ) to the ML development hub  100 , e.g., in a same cluster as ML development hub  100 , or available within a same organization (on premises  282 ), or available via cloud services (cloud  283 ). 
     With an SDK  271  such as depicted in  FIG.  2   , ML developers can coordinate across compute, storage, open source and data using single components of platform layer  200  to access the access and compute infrastructure  280 . This enables data science teams to deploy AI &amp; ML models at scale, allowing them to re-use known solutions, while at the same time contributing solutions to a central marketplace of re-usable products. Users can develop models on their local machines, train them using graphics processing units (GPUs) on a cloud and serve them in any desired private or public cloud with optimal tools such as Airflow and others, as illustrated. 
     The SDK  271  can enable options for leveraging existing re-usable productivity tools and frameworks during entire ML lifecycles, from data collection, pre-processing, training, testing, and experimentation, to data visualization. The SDK  271  can furthermore enable rapid experimentation and testing of ML models to analyze the performance of the ML models. 
       FIG.  3    illustrates an example software development kit (SDK) layer of a machine learning development hub, in accordance with one or more embodiments described herein. The example SDK  300  can implement, e.g., the SDK  271  introduced in  FIG.  2   . The SDK  300  can receive inputs  301  from platform layer  200 , e.g., the platform layer  200  shown in  FIG.  2   , and the SDK  300  can produce the inputs  301 A for further processing by the CaaS  272 , e.g., the CaaS  272  shown in  FIG.  2   . The inputs  301  can comprise, e.g., inputs such as  101 ,  102 ,  103  shown in  FIG.  1   , and the inputs  301 A can comprise, e.g., inputs such as  101 A,  102 A,  102 B, and  103 A shown in  FIG.  1   . 
     The example SDK  300  generally comprises a platform API  310  configured for interactions with the platform layer  200 , and an orchestrator API  320  configured to interact with the platform API  310 , orchestrate translations, and interact with downstream infrastructure such as CaaS  272 . The example platform API  310  can comprise components such as workspace  311 , data  312 , jobs  313 , pipeline  314 , model  315 , and GPU  316 . The example orchestrator API  320  can comprise components such as cluster  321 , namespace  322 , volume  323 , deployment  324 , worker  325 , and image  326 . 
     The platform API  310  can interact with artifact storage  330 , wherein artifact storage  330  can store artifacts such as images for use in provisioning a platform layer  200  user interface. The orchestrator API  320  can interact with machine learning object store  380 . The platform API  310  and the orchestrator API  320  can interact with identity platform  350 , wherein the identity platform  350  can comprise, e.g., a Koa identity platform or other similar technology that can implement security  130 , introduced in  FIG.  1   . The identity platform  350 , data artifacts  360 , and identity  370  can all be continuously updated by development security operations continuous integration, continuous delivery (Cl/CD)  340 . 
     In general, with regard to  FIG.  3   , the SDK  300  can be used to create workspaces, run jobs, and/or run services using a domain language. Any credentials required to securely access various components of the infrastructure (e.g., access and compute infrastructure  280 ) can be stored in a key-value store, e.g., a Vault key-value store. Images used for provisioning workspaces, images, pipelines, etc. can be stored in a registry service, e.g. a Harbor registry service. Interactions between components in the APIs  310 ,  320  can also secured using the identity platform  350 . 
     With Cl/CD practices enforced while deploying and delivering feature improvements for modules, the core components of a shared library provided by SDK  300  can be the platform API  310  and the orchestrator API  320 . With regard to the platform API  310 , requests sent by a data practitioner can be routed via the platform API  310 , which can classify the request type sent by the user and store metadata associated with the request in a data store. 
     With regard to the orchestrator API  320 , based on requests received from platform API  310 , orchestrator API  320  can interact with CaaS  272 , e.g., a KubeAPI server of a Kubernetes cluster, in order to fulfill specifications for creating workspaces, accessing services or frameworks within the infrastructure  280 , or launching jobs using CPUs or GPUs. A set of controllers in the orchestrator API  320  can execute containers within CaaS  272  as needed to complete the pipeline specified by the user. 
     With regard to managing ML artifacts, data artifacts  360  can be configured to assist data scientist management of personal buckets, which the SDK  300  can provide as a part of each user&#39;s workspace. Typical moments, or stages, where users would rely on buckets include, after transforming a sample dataset, when they version it and upload it to the bucket (labeling the artifact as a dataset); after training a model, when they pickle it and upload it to the bucket (labeling the artifact as a model); during model drift analysis, when they download a current model from a bucket to compare results; and to publish a model as an API from a different environment, when they download a best model from a bucket for exposure. In order to access a storage piece of the infrastructure, data scientists can import artifacts into the SDK  300  and pass a token to authorize a user to access to the object storage. This ensures that user has sufficient privileges to simply upload and download data from their machine learning object store  380 . 
     With regard to training of ML models and the computing resources, such as GPUs, used for such training, the jobs  313  and GPU  316  modules can help data scientists train models faster by allowing users to burst out code that requires significant computational power. With a valid private token, the jobs  313  module can be configured to directly deploy code to execute to Kubernetes clusters within CaaS  272 . Users can then check their logs on the clusters, e.g., by using methods such as training.logs( ), and training.status( ) methods. Similarly, the GPU  316  module can be used to deploy models in a GPU farm. In some embodiments, training also can be done in parallel while using the SDK  300  to fine-tune user models. 
       FIG.  3    can provide a novel framework that supports a diversity of analytical tools, model registries, metadata and data stores, model serving and workflows pieced together.  FIG.  3    can streamline access to compute and data storage, thus helping data science teams to launch their business applications faster from inception to production.  FIG.  3    can also support reusable architecture and efficiencies in line-of-sight with GPU pooling and data cold storage strategies. Novel elements of the illustrated framework bring to bear automated model building, serving, deployment, storage and inference over time, over a single common framework supported by the SDK  300 . 
       FIG.  4    illustrates example provisioning of artifacts to a machine learning development hub user interface, in accordance with one or more embodiments described herein.  FIG.  4    illustrates an example platform UI  400  comprising various tools, e.g. tool  410 , tool  420 , and tool  430 . Each of the tools  410 ,  420 , and  430  can make use of an artifact, for example tool  410  can make use of artifact  411 , tool  420  can make use of artifact  421 , and tool  430  can make use of artifact  431 . The artifacts  411 ,  421 ,  431  can comprise, e.g. images or other data employed by UI elements of tools  410 ,  420 , and  430 . 
       FIG.  4    furthermore illustrates platform API  310  and artifact storage  330 , previously introduced in  FIG.  3   . The platform UI  400  can be supported by platform  200 , also illustrated in  FIG.  3   . The platform UI  400  can be configured to provide identifiers (IDs)  451  to the platform API  310 . The IDs  451  can correspond to the tools  410 ,  420 , and  430  or the artifacts  411 ,  421 ,  431 . The platform API  310  can be configured to use the IDs  451  to retrieve the artifacts  411 ,  421 ,  431  from the artifact storage  330 , and the platform API  310  can supply the artifacts  411 ,  421 ,  431  to the platform UI  400  in order to provision the platform UI  400  and tools  410 ,  420 , and  430 . 
       FIG.  5    illustrates example mediation of communications between microservices, in accordance with one or more embodiments described herein.  FIG.  5    includes a platform layer  500  comprising example microservices  501  and  502 , an SDK  520  comprising example configurable translators  521  and  522 , and an infrastructure layer  530  comprising example microservices  531  and  532 . 
     In  FIG.  5   , the platform layer  500  can implement, e.g., a platform layer  110  or  200 . The SDK  520  can implement, e.g., an SDK  271  or  300 . The infrastructure layer  530  can implement, e.g., the access and compute infrastructure  280 . Platform layer  500  can include or reference microservices  501  and  502 , which correspond to microservices  531  and  532  supported in the infrastructure layer  530 . 
       FIG.  5    illustrates an SDK  520  that can be equipped with configurable translators  521 ,  522  that can be updated as needed to support translation of inputs  511 ,  512  associated with microservices  501 ,  502  in the platform layer  500  into inputs  511 A,  512 A provided to microservices  531 ,  532  in the infrastructure layer  530 . Furthermore, the SDK  520  can be configured to automatically or semi-automatically translate inputs  511 ,  512  into inputs  511 A,  512 A and provide inputs  511 A,  512 A to corresponding microservices  531 ,  532 . In some embodiments, the SDK  520  can secure transmissions of inputs  511 A,  512 A using dynamically generated encryption keys. 
       FIG.  6    illustrates example security functions of a machine learning development hub, in accordance with one or more embodiments described herein.  FIG.  6    includes a platform layer  600  comprising example tools  601  and  602 , an example SDK  620  comprising security  621 , encryption key generator  622 , and credential data store  623 , and an infrastructure layer  630  comprising example tools  631  and  632 . 
     In  FIG.  6   , the platform layer  600  can implement, e.g., a platform layer  110  or  200 . The SDK  620  can implement, e.g., an SDK  271  or  300 . The infrastructure layer  630  can implement, e.g., the access and compute infrastructure  280 . Platform layer  600  can include or reference tools  601  and  602 , which correspond to tools  631  and  632  supported in the infrastructure layer  630 . 
       FIG.  6    illustrates an SDK  620  comprising a credential data store  623  and security  621  configured to use the credential data store  623  to access infrastructure layer  630 . The credential data store  623  can be implemented, e.g., as a key-value store as described herein. When SDK  620  provides inputs, e.g., inputs  612 A, to an element of infrastructure layer  630  such as tool  632 , security  621  can use appropriate corresponding credentials (corresponding to tool  632 ) to access a user account for use with tool  632 . After gaining access to the user account, the SDK  620  can supply the inputs  612 A to the tool  632 . 
     In another aspect,  FIG.  6    illustrates an SDK  620  comprising an encryption key generator  622  that can be used to dynamically secure transmissions of inputs  611 A,  612 A to components of the infrastructure layer  630 . The encryption key generator  622  can dynamically generate encryption keys for use with different discrete transactions between the SDK  620  and the infrastructure layer  630 . 
       FIG.  7    is a flow diagram of an example, non-limiting computer implemented method that can be performed by a machine learning development hub, in accordance with one or more embodiments described herein. The blocks of the illustrated method represent operations according to a method, components in one or more computing devices, and/or computer executable instructions in a computer readable storage medium, as can be appreciated. While the operations are illustrated in sequence, it can furthermore be appreciated that certain operations can optionally be re-ordered, combined, removed or supplemented with other operations in some embodiments. 
     In an embodiment, the method illustrated in  FIG.  7    can be performed by a server or cluster that provides a machine learning development hub, such as the machine learning development hub  100  illustrated in  FIG.  1   . Operation  702  comprises storing, by a server, an image associated with a second machine learning development platform. For example, an image associated with any of the infrastructure platforms  141 ,  142 ,  143  can be stored in an artifact storage  330 . 
     Operation  704  comprises using, by the server, the image to provision a user interface for a first machine learning development platform. The image stored in artifact storage  330  can be used to provision a user interface such as platform UI  400 . 
     Operation  706  comprises obtaining, by the server, a user credential to securely access the second machine learning development platform. For example, a user credential can be obtained to securely access infrastructure platform  141 . The user credential can be obtained from a user, e.g. as a user input to selector  114 . In some embodiments, the user credential can be stored in a credential data store  623 , which can optionally be implemented as a key-value store. 
     Operation  708  comprises receiving, by the server, an input via the user interface. For example, any of inputs  111 ,  112 ,  113  can be received via the platform UI  400 . The input can comprise, e.g., training data usable to train a machine learning model, a data modification instruction to modify training data usable to train a machine learning model, a compute instruction that specifies compute operations to be performed by a machine learning model, and/or a performance monitoring instruction that specifies performance monitoring of a machine learning model. 
     Operation  710  comprises translating the input, by the server, resulting in a translated input. Operation  710  is optional as not all inputs need be translated. A translator  121 ,  122 ,  123  can be used to translate an input  101 ,  102 ,  103  into a translated input  101 A,  102 A,  102 B,  103 A. In some embodiments, an architecture such as illustrated in  FIG.  3    can be used to effect translation as needed. 
     Operation  712  comprises supplying, by the server, the input (as optionally translated at block  710 ) to the second machine learning development platform, wherein the supplying is secured by the user credential. For example, the ML development hub  100  can supply any of translated inputs  101 A,  102 A,  102 B,  103 A to any of infrastructure platforms  141 ,  142 ,  143 . The supplying can be secured by the user credential obtained at block  706 . In some embodiments, operation  712  can be performed automatically, i.e., without user initiation other than providing an input  101  to the platform layer  110 . ML development hub  100  can automatically initiate operation  712  in response to receiving the input  101 . 
     Furthermore, in some embodiments, as shown in  FIG.  5   , supplying the input, e.g., translated inputs  511 A to the second machine learning development platform (infrastructure layer  530 ) can comprise supplying, via a first microservice  501  associated with the first machine learning development platform (platform layer  500 ), the input  511 A to a second microservice  531  associated with the second machine learning development platform (infrastructure layer  530 ). Also, as shown in  FIG.  6   , supplying an input  611 A to a second machine learning development platform (at infrastructure layer  630 ) can be further secured by an encryption key generated by encryption key generator  622  in response to receiving the input  611 . 
       FIG.  8    is a flow diagram of another example, non-limiting computer implemented method that can be performed by a machine learning development hub, in accordance with one or more embodiments described herein. The blocks of the illustrated method represent operations according to a method, components in one or more computing devices, and/or computer executable instructions in a computer readable storage medium, as can be appreciated. While the operations are illustrated in sequence, it can furthermore be appreciated that certain operations can optionally be re-ordered, combined, removed or supplemented with other operations in some embodiments. 
     In an embodiment, the method illustrated in  FIG.  8    can be performed by a server or cluster that provides a machine learning development hub, such as the machine learning development hub  100  illustrated in  FIG.  1   . Example operation  802  comprises receiving an input for a first machine learning development platform. For example, an input  101  can be received for platform layer  110 . The input  101  can be received via a user interface for the first machine learning development platform, such as platform UI  400 . 
     Operations  804 ,  806 , and  808  can be performed in response to receiving the input  101  at block  802 . Example operation  804  comprises translating the input  101 , resulting in a translated input  101 A. Operation  804  can be optional as noted above regarding operation  710 . 
     Example operation  806  comprises generating an encryption key. For example, an encryption key generator  622  can be used to dynamically generate an encryption key. Example operation  808  comprises using the encryption key to securely supply the input  101  or  101 A to a second machine learning development platform, e.g., to infrastructure platform  141 . In some embodiments, securely supplying the input  101  or  101 A to the second machine learning development platform  141  can comprise operations such as illustrated in  FIG.  5   , e.g., supplying, using a first microservice  501  associated with the first machine learning development platform  500 , the input  101  or  101 A to a second microservice  531  associated with the second machine learning development platform  141 . 
       FIG.  9    is a flow diagram of another example, non-limiting computer implemented method that can be performed by a machine learning development hub, in accordance with one or more embodiments described herein. The blocks of the illustrated method represent operations according to a method, components in one or more computing devices, and/or computer executable instructions in a computer readable storage medium, as can be appreciated. While the operations are illustrated in sequence, it can furthermore be appreciated that certain operations can optionally be re-ordered, combined, removed or supplemented with other operations in some embodiments. 
     In an embodiment, the method illustrated in  FIG.  9    can be performed by a server or cluster that provides a machine learning development hub, such as the machine learning development hub  100  illustrated in  FIG.  1   . Example operation  902  comprises provisioning a user interface for a first machine learning development platform with a stored image associated with a second machine learning development platform. For example, a platform UI  400  for a platform layer  200  can be provisioned with an image stored in artifact storage  330 , wherein the image can be associated with a second machine learning development platform, such as infrastructure platform  141 . 
     Example operation  904  comprises collecting a credential to securely access the second machine learning development platform. For example, a credential can be collected via a user input and stored in the credential data storage  623 . 
     Example operation  906  comprises receiving an input for the first machine learning development platform. For example, an input  101  can be received for the machine learning development platform enabled by platform layer  110 . The input  101  can comprise, e.g., at least one of training data for training a machine learning model, a data modification instruction to modify training data employable to train the machine learning model, or any of the other various potential inputs described herein. In some embodiments, receiving the input  101  for the first machine learning development platform can be conducted via a first API, such as platform API  310 . 
     Operations  908 - 912  can be performed automatically without direct user initiation, in response to receiving the input at block  906 . Example operation  908  comprises translating the input  101 , resulting in a translated input  101 A. Example operation  908  can be optional as explained above. Example operation  910  comprises generating a one-time use encryption key. For example, encryption key generator  622  can be activated to generate the one-time use encryption key. 
     Example operation  912  comprises supplying the translated input  101 A to a second machine learning development platform, e.g., to infrastructure platform  141 . Operation  912  can be secured by the encryption key generated at block  910 . Supplying the translated input  101 A to the second machine learning development platform  141  can also be secured by the credential collected at block  904 . In some embodiments, translating the input  101  and supplying the translated input  101 A to the second machine learning development platform  141  can be conducted via a second API, e.g., the orchestrator API  320  illustrated in  FIG.  3   . 
     In order to provide additional context for various embodiments described herein,  FIG.  10    and the following discussion are intended to provide a brief, general description of a suitable computing environment  1000  in which the various embodiments of the embodiment described herein can be implemented. While the embodiments have been described above in the general context of computer-executable instructions that can run on one or more computers, those skilled in the art will recognize that the embodiments can be also implemented in combination with other program modules and/or as a combination of hardware and software. 
     Generally, program modules include routines, programs, components, data structures, etc., that perform particular tasks or implement particular abstract data types. Moreover, those skilled in the art will appreciate that the methods can be practiced with other computer system configurations, including single-processor or multiprocessor computer systems, minicomputers, mainframe computers, IoT devices, distributed computing systems, as well as personal computers, hand-held computing devices, microprocessor-based or programmable consumer electronics, and the like, each of which can be operatively coupled to one or more associated devices. 
     The embodiments illustrated herein can be also practiced in distributed computing environments where certain tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules can be located in both local and remote memory storage devices. 
     Computing devices typically include a variety of media, which can include computer-readable storage media, machine-readable storage media, and/or communications media, which two terms are used herein differently from one another as follows. Computer-readable storage media or machine-readable storage media can be any available storage media that can be accessed by the computer and includes both volatile and nonvolatile media, removable and non-removable media. By way of example, and not limitation, computer-readable storage media or machine-readable storage media can be implemented in connection with any method or technology for storage of information such as computer-readable or machine-readable instructions, program modules, structured data or unstructured data. 
     Computer-readable storage media can include, but are not limited to, random access memory (RAM), read only memory (ROM), electrically erasable programmable read only memory (EEPROM), flash memory or other memory technology, compact disk read only memory (CD-ROM), digital versatile disk (DVD), Blu-ray disc (BD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, solid state drives or other solid state storage devices, or other tangible and/or non-transitory media which can be used to store desired information. In this regard, the terms “tangible” or “non-transitory” herein as applied to storage, memory or computer-readable media, are to be understood to exclude only propagating transitory signals per se as modifiers and do not relinquish rights to all standard storage, memory or computer-readable media that are not only propagating transitory signals per se. 
     Computer-readable storage media can be accessed by one or more local or remote computing devices, e.g., via access requests, queries or other data retrieval protocols, for a variety of operations with respect to the information stored by the medium. 
     Communications media typically embody computer-readable instructions, data structures, program modules or other structured or unstructured data in a data signal such as a modulated data signal, e.g., a carrier wave or other transport mechanism, and includes any information delivery or transport media. The term “modulated data signal” or signals refers to a signal that has one or more of its characteristics set or changed in such a manner as to encode information in one or more signals. By way of example, and not limitation, communication media include wired media, such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media. 
     With reference again to  FIG.  10   , the example environment  1000  for implementing various embodiments of the aspects described herein includes a computer  1002 , the computer  1002  including a processing unit  1004 , a system memory  1006  and a system bus  1008 . The system bus  1008  couples system components including, but not limited to, the system memory  1006  to the processing unit  1004 . The processing unit  1004  can be any of various commercially available processors and may include a cache memory. Dual microprocessors and other multi-processor architectures can also be employed as the processing unit  1004 . 
     The system bus  1008  can be any of several types of bus structure that can further interconnect to a memory bus (with or without a memory controller), a peripheral bus, and a local bus using any of a variety of commercially available bus architectures. The system memory  1006  includes ROM  1010  and RAM  1012 . A basic input/output system (BIOS) can be stored in a non-volatile memory such as ROM, erasable programmable read only memory (EPROM), EEPROM, which BIOS contains the basic routines that help to transfer information between elements within the computer  1002 , such as during startup. The RAM  1012  can also include a high-speed RAM such as static RAM for caching data. 
     The computer  1002  further includes an internal hard disk drive (HDD)  1014  (e.g., EIDE, SATA), one or more external storage devices  1016  (e.g., a magnetic floppy disk drive (FDD)  1016 , a memory stick or flash drive reader, a memory card reader, etc.) and an optical disk drive  1020  (e.g., which can read or write from a CD-ROM disc, a DVD, a BD, etc.). While the internal HDD  1014  is illustrated as located within the computer  1002 , the internal HDD  1014  can also be configured for external use in a suitable chassis (not shown). Additionally, while not shown in environment  1000 , a solid state drive (SSD) could be used in addition to, or in place of, an HDD  1014 . The HDD  1014 , external storage device(s)  1016  and optical disk drive  1020  can be connected to the system bus  1008  by an HDD interface  1024 , an external storage interface  1026  and an optical drive interface  1028 , respectively. The interface  1024  for external drive implementations can include at least one or both of Universal Serial Bus (USB) and Institute of Electrical and Electronics Engineers (IEEE) 1394 interface technologies. Other external drive connection technologies are within contemplation of the embodiments described herein. 
     The drives and their associated computer-readable storage media provide nonvolatile storage of data, data structures, computer-executable instructions, and so forth. For the computer  1002 , the drives and storage media accommodate the storage of any data in a suitable digital format. Although the description of computer-readable storage media above refers to respective types of storage devices, it should be appreciated by those skilled in the art that other types of storage media which are readable by a computer, whether presently existing or developed in the future, could also be used in the example operating environment, and further, that any such storage media can contain computer-executable instructions for performing the methods described herein. 
     A number of program modules can be stored in the drives and RAM  1012 , including an operating system  1030 , one or more application programs  1032 , other program modules  1034  and program data  1036 . All or portions of the operating system, applications, modules, and/or data can also be cached in the RAM  1012 . The systems and methods described herein can be implemented utilizing various commercially available operating systems or combinations of operating systems. 
     Computer  1002  can optionally comprise emulation technologies. For example, a hypervisor (not shown) or other intermediary can emulate a hardware environment for operating system  1030 , and the emulated hardware can optionally be different from the hardware illustrated in  FIG.  10   . In such an embodiment, operating system  1030  can comprise one virtual machine (VM) of multiple VMs hosted at computer  1002 . Furthermore, operating system  1030  can provide runtime environments, such as the Java runtime environment or the .NET framework, for applications  1032 . Runtime environments are consistent execution environments that allow applications  1032  to run on any operating system that includes the runtime environment. Similarly, operating system  1030  can support containers, and applications  1032  can be in the form of containers, which are lightweight, standalone, executable packages of software that include, e.g., code, runtime, system tools, system libraries and settings for an application. 
     Further, computer  1002  can comprise a security module, such as a trusted processing module (TPM). For instance with a TPM, boot components hash next in time boot components, and wait for a match of results to secured values, before loading a next boot component. This process can take place at any layer in the code execution stack of computer  1002 , e.g., applied at the application execution level or at the operating system (OS) kernel level, thereby enabling security at any level of code execution. 
     A user can enter commands and information into the computer  1002  through one or more wired/wireless input devices, e.g., a keyboard  1038 , a touch screen  1040 , and a pointing device, such as a mouse  1042 . Other input devices (not shown) can include a microphone, an infrared (IR) remote control, a radio frequency (RF) remote control, or other remote control, a joystick, a virtual reality controller and/or virtual reality headset, a game pad, a stylus pen, an image input device, e.g., camera(s), a gesture sensor input device, a vision movement sensor input device, an emotion or facial detection device, a biometric input device, e.g., fingerprint or iris scanner, or the like. These and other input devices are often connected to the processing unit  1004  through an input device interface  1044  that can be coupled to the system bus  1008 , but can be connected by other interfaces, such as a parallel port, an IEEE 1394 serial port, a game port, a USB port, an IR interface, a BLUETOOTH® interface, etc. 
     A monitor  1046  or other type of display device can be also connected to the system bus  1008  via an interface, such as a video adapter  1048 . In addition to the monitor  1046 , a computer typically includes other peripheral output devices (not shown), such as speakers, printers, etc. 
     The computer  1002  can operate in a networked environment using logical connections via wired and/or wireless communications to one or more remote computers, such as a remote computer(s)  1050 . The remote computer(s)  1050  can be a workstation, a server computer, a router, a personal computer, portable computer, microprocessor-based entertainment appliance, a peer device or other common network node, and typically includes many or all of the elements described relative to the computer  1002 , although, for purposes of brevity, only a memory/storage device  1052  is illustrated. The logical connections depicted include wired/wireless connectivity to a local area network (LAN)  1054  and/or larger networks, e.g., a wide area network (WAN)  1056 . Such LAN and WAN networking environments are commonplace in offices and companies, and facilitate enterprise-wide computer networks, such as intranets, all of which can connect to a global communications network, e.g., the internet. 
     When used in a LAN networking environment, the computer  1002  can be connected to the local network  1054  through a wired and/or wireless communication network interface or adapter  1058 . The adapter  1058  can facilitate wired or wireless communication to the LAN  1054 , which can also include a wireless access point (AP) disposed thereon for communicating with the adapter  1058  in a wireless mode. 
     When used in a WAN networking environment, the computer  1002  can include a modem  1060  or can be connected to a communications server on the WAN  1056  via other means for establishing communications over the WAN  1056 , such as by way of the internet. The modem  1060 , which can be internal or external and a wired or wireless device, can be connected to the system bus  1008  via the input device interface  1044 . In a networked environment, program modules depicted relative to the computer  1002  or portions thereof, can be stored in the remote memory/storage device  1052 . It will be appreciated that the network connections shown are example and other means of establishing a communications link between the computers can be used. 
     When used in either a LAN or WAN networking environment, the computer  1002  can access cloud storage systems or other network-based storage systems in addition to, or in place of, external storage devices  1016  as described above. Generally, a connection between the computer  1002  and a cloud storage system can be established over a LAN  1054  or WAN  1056  e.g., by the adapter  1058  or modem  1060 , respectively. Upon connecting the computer  1002  to an associated cloud storage system, the external storage interface  1026  can, with the aid of the adapter  1058  and/or modem  1060 , manage storage provided by the cloud storage system as it would other types of external storage. For instance, the external storage interface  1026  can be configured to provide access to cloud storage sources as if those sources were physically connected to the computer  1002 . 
     The computer  1002  can be operable to communicate with any wireless devices or entities operatively disposed in wireless communication, e.g., a printer, scanner, desktop and/or portable computer, portable data assistant, communications satellite, any piece of equipment or location associated with a wirelessly detectable tag (e.g., a kiosk, news stand, store shelf, etc.), and telephone. This can include Wireless Fidelity (Wi-Fi) and BLUETOOTH® wireless technologies. Thus, the communication can be a predefined structure as with a conventional network or simply an ad hoc communication between at least two devices. 
     The above description includes non-limiting examples of the various embodiments. It is, of course, not possible to describe every conceivable combination of components or methodologies for purposes of describing the disclosed subject matter, and one skilled in the art may recognize that further combinations and permutations of the various embodiments are possible. The disclosed subject matter is intended to embrace all such alterations, modifications, and variations that fall within the spirit and scope of the appended claims. 
     With regard to the various functions performed by the above described components, devices, circuits, systems, etc., the terms (including a reference to a “means”) used to describe such components are intended to also include, unless otherwise indicated, any structure(s) which performs the specified function of the described component (e.g., a functional equivalent), even if not structurally equivalent to the disclosed structure. In addition, while a particular feature of the disclosed subject matter may have been disclosed with respect to only one of several implementations, such feature may be combined with one or more other features of the other implementations as may be desired and advantageous for any given or particular application. 
     The terms “exemplary” and/or “demonstrative” as used herein are intended to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as “exemplary” and/or “demonstrative” is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent structures and techniques known to one skilled in the art. Furthermore, to the extent that the terms “includes,” “has,” “contains,” and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive—in a manner similar to the term “comprising” as an open transition word—without precluding any additional or other elements. 
     The term “or” as used herein is intended to mean an inclusive “or” rather than an exclusive “or.” For example, the phrase “A or B” is intended to include instances of A, B, and both A and B. Additionally, the articles “a” and “an” as used in this application and the appended claims should generally be construed to mean “one or more” unless either otherwise specified or clear from the context to be directed to a singular form. 
     The term “set” as employed herein excludes the empty set, i.e., the set with no elements therein. Thus, a “set” in the subject disclosure includes one or more elements or entities. Likewise, the term “group” as utilized herein refers to a collection of one or more entities. 
     The terms “first,” “second,” “third,” and so forth, as used in the claims, unless otherwise clear by context, is for clarity only and doesn&#39;t otherwise indicate or imply any order in time. For instance, “a first determination,” “a second determination,” and “a third determination,” does not indicate or imply that the first determination is to be made before the second determination, or vice versa, etc. 
     The description of illustrated embodiments of the subject disclosure as provided herein, including what is described in the Abstract, is not intended to be exhaustive or to limit the disclosed embodiments to the precise forms disclosed. While specific embodiments and examples are described herein for illustrative purposes, various modifications are possible that are considered within the scope of such embodiments and examples, as one skilled in the art can recognize. In this regard, while the subject matter has been described herein in connection with various embodiments and corresponding drawings, where applicable, it is to be understood that other similar embodiments can be used or modifications and additions can be made to the described embodiments for performing the same, similar, alternative, or substitute function of the disclosed subject matter without deviating therefrom. Therefore, the disclosed subject matter should not be limited to any single embodiment described herein, but rather should be construed in breadth and scope in accordance with the appended claims below.