Headstart for data scientists

A method, system, and apparatus are provided for recommending machine learning (ML) project resources for completing a user project by generating indexed project metadata for a plurality of ML projects, generating search metadata for a search request for ML project resources to develop an ML project, and then evaluating the search metadata against the indexed project metadata for each ML project to form a relevancy assessment which is used to order trained models from the ML projects and to display one or more recommended ML project resources comprising one or more of the plurality of trained models having a relevancy assessment exceeding a relevancy threshold.

BACKGROUND OF THE INVENTION

In the field of artificially intelligent computer systems, cognitive computing systems (such as the IBM Watson™ artificially intelligent computer system or and other natural language question answering systems) simulate human thought processes by using computerized models and self-learning systems, such as data mining, pattern recognition and natural language processing to mimic the way the human brain functions. Increasingly, cognitive services (e.g., visual recognition, conversation, speech to text, language translation, etc.) are provided which package cognitive computing machine learning algorithms into discrete components that are easy for users to interface with their applications. Today's cognitive services are bundled with default machine learning models that have been trained on a set of representative training data (referred to as ground truth), which typically covers a wide range of topics. For a user's application to gain full benefit of a cognitive service, the user should train the cognitive service's machine learning model to learn the specifics of the problem from sample labeled data and help make predictions on unlabeled data by using a “training process” which involves providing the machine learning model with a representative data set of inputs and corresponding outputs. For example, if user's application pertains to a specific topic (e.g., financial, travel, healthcare, etc.), the user should train the machine learning model with question/answer pairs relevant to the specific industry so that the cognitive service learns by example from pairs of representative inputs/outputs which constitute the “ground truth” for the system, thereby improving the accuracy of the corresponding cognitive service.

In recent years, the number of machine learning models and associated datasets has grown exponentially, but this proliferation of data has the unfortunate side effect of making it increasingly difficult to find the data, models, and techniques that are relevant for building successful data science projects. For example, in a large machine learning development environment, like Watson Studio, there may be thousands of such projects and data sets available, but there is no efficient and accurate way to determine if someone is already working in the same or similar domain, or if there are any existing machine learning models or ground truth or techniques that might expedite development of a user's project. Conventional solutions for using a basic keyword search of existing models or data sets is not sufficient to find deeply relevant models and data sets that can help the user with the data science work. As a result, the existing solutions for finding data science models or assets solutions that are relevant to a user's project are extremely difficult at a practical level since there is no tool that is able to find relevant resources for a project taking into account the particulars of a given problem statement and data set.

SUMMARY

Broadly speaking, selected embodiments of the present disclosure provide an information handling system, method, and apparatus for identifying machine learning project resources that are relevant to a user's modeling problem by performing multiple tests using a cognitive service on multiple trained machine learning models and datasets to find matching machine learning project resources and recommendations based on user data and/or search query information. For each of a plurality of catalogued machine learning models and datasets, an analysis, such as natural language processing and analytical analysis, is performed on the catalogued machine learning models and datasets to assemble indexed metadata for identifying one or more elements used to build the machine learning models and datasets. In addition, a search query is constructed based on user-specified query parameters, received sample data from the user, and/or extracted search metadata. The information handling system performs a search of the indexed metadata using the search query to find matching machine learning project resources based on metadata commonalities. In selected embodiments, the matching project resources may be ranked or ordered for relevancy to the user's needs, such as by using machine learning algorithms to assess entity matching in the domain(s) of interest. In addition, the information handling system presents the matching machine learning project resources as search results to the user, alone or in combination with insights or recommendations that are based on identified differences between user content and each returned result. Example recommendations may include an entity expansion suggestion (e.g., for models with similar domains, the recommendation may suggest expanding with other entities seen in those models) and/or a dataset tuning suggestion (e.g., recommending dataset adjustments based on models with similar dataset features).

DETAILED DESCRIPTION

The present invention may be a system, a method, and/or a computer program product. In addition, selected aspects of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and/or hardware aspects that may all generally be referred to herein as a “circuit,” “module” or “system.” Furthermore, aspects of the present invention may take the form of computer program product embodied in a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention.

FIG. 1illustrates information handling system100, more particularly, a processor and common components, which is a simplified example of a computer system capable of performing the computing operations described herein. Information handling system100includes one or more processors110coupled to processor interface bus112. Processor interface bus112connects processors110to Northbridge115, which is also known as the Memory Controller Hub (MCH). Northbridge115connects to system memory120and provides a means for processor(s)110to access the system memory. In the system memory120, a variety of programs may be stored in one or more memory devices, including a machine learning project resource locator module221which may be invoked to analyze a user problem statement and dataset to find ML project resources, such as data science models/assets, that can provide a head start for a user solution. Graphics controller125also connects to Northbridge115. In one embodiment, Peripheral Component Interconnect (PCI) Express bus118connects Northbridge115to graphics controller125. Graphics controller125connects to display device130, such as a computer monitor.

ExpressCard155is a slot that connects hot-pluggable devices to the information handling system. ExpressCard155supports both PCI Express and Universal Serial Bus (USB) connectivity as it connects to Southbridge135using both the USB and the PCI Express bus. Southbridge135includes USB Controller140that provides USB connectivity to devices that connect to the USB. These devices include webcam (camera)150, infrared (IR) receiver148, keyboard and trackpad144, and Bluetooth device146, which provides for wireless personal area networks (PANs). USB Controller140also provides USB connectivity to other miscellaneous USB connected devices142, such as a mouse, removable nonvolatile storage device145, modems, network cards, Integrated Services Digital Network (ISDN) connectors, fax, printers, USB hubs, and many other types of USB connected devices. While removable nonvolatile storage device145is shown as a USB-connected device, removable nonvolatile storage device145could be connected using a different interface, such as a Firewire interface, etc.

WhileFIG. 1shows one information handling system, an information handling system may take many forms. For example, an information handling system may take the form of a desktop, server, portable, laptop, notebook, or other form factor computer or data processing system. In addition, an information handling system may take other form factors such as a personal digital assistant (PDA), a gaming device, Automated Teller Machine (ATM), a portable telephone device, a communication device or other devices that include a processor and memory. In addition, an information handling system need not necessarily embody the north bridge/south bridge controller architecture, as it will be appreciated that other architectures may also be employed.

FIG. 2depicts a network environment200in which an information handling system uses a machine learning project resource locator in accordance with selected embodiments of the present disclosure. Types of information handling systems range from small handheld devices, such as handheld computer/mobile telephone210to large mainframe systems, such as mainframe computer270. Examples of handheld computer210include personal digital assistants (PDAs), personal entertainment devices, such as Moving Picture Experts Group Layer-3 Audio (MP3) players, portable televisions, and compact disc players. Other examples of information handling systems include pen, or tablet, computer220, laptop or notebook computer230, workstation240, personal computer system250, and server260. Other types of information handling systems that are not individually shown inFIG. 2are represented by information handling system280. As shown, the various information handling systems can be networked together using computer network200. Types of computer network that can be used to interconnect the various information handling systems include Local Area Networks (LANs), Wireless Local Area Networks (WLANs), the Internet, the Public Switched Telephone Network (PSTN), other wireless networks, and any other network topology that can be used to interconnect the information handling systems. Many of the information handling systems include nonvolatile data stores, such as hard drives and/or nonvolatile memory. The embodiment of the information handling system shown inFIG. 2includes separate nonvolatile data stores (more specifically, server260utilizes nonvolatile data store265, mainframe computer270utilizes nonvolatile data store275, and information handling system280utilizes nonvolatile data store285). The nonvolatile data store can be a component that is external to the various information handling systems or can be internal to one of the information handling systems. In addition, removable nonvolatile storage device145can be shared among two or more information handling systems using various techniques, such as connecting the removable nonvolatile storage device145to a USB port or other connector of the information handling systems.

As discussed above, there are an abundance of cognitive services bundled or bound to machine learning models that have been trained and proven on a set of ground truth (e.g., question/answer pairs) that is specific to a topic or category or that are trained on broad categories of information. The ability to use keyword searches to evaluate a database or catalog of ML models and datasets to find ML project resources that are relevant to a new project is not effective or efficient, especially with large machine learning development environments where there may be thousands of such projects and data sets available. To facilitate the ability of a user to effectively locate ML project resources for use in building machine learning models, there is disclosed herein an approach that can be executed on an information handling system that allows users to provide sample data and/or search queries to a ML project analysis system which identifies and returns the best matching machine learning models and datasets, alone or in combination with suggestions about how the matching resources can help with modeling a current data set. In one embodiment, the ML project analysis system applies natural language processing (NLP) and analytic analysis to catalogued ML models and training data to categorize the models into aspects including domain, entities, and relationships between the entities. Responsive to receiving a request from a user to assist with developing an ML project, the ML project analysis system analyzes the request to determine keywords, intents, and a domain, and then associates the keywords, the intents, and the domain with indexed metadata from the categorized models/datasets to form a relevancy assessment which is used to order the categorized models for presentation to the user. In selected embodiments, the relevancy assessment uses ML algorithms to entity matching between the user's search request and the catalogued ML models and in one or more domains of interest.

The ML project analysis system may also provide a user interface (UI) representation of the search results with matching categorized models, associated relevancy assessment, and keyword mapping which allows the user to adjust prioritization based on entries in the representation. Responsive to user selection/adjustment, the ML project analysis system may update the presentation of matching ML project resources to better fit the user needs. In one embodiment, the ML project analysis system leverages cloud and machine learning model containers in hosting an exchange of a variety of machine learning models and datasets and instrumenting a solution that identifies the best-suited machine learning models and datasets to each user data set or search query based on an evaluation the ML project analysis system runs against that sample data.

To provide additional details for an improved understanding of selected embodiments of the present disclosure, reference is now made toFIG. 3which is a diagrammatical depiction of a machine learning development environmet300which uses a machine learning (ML) project analysis system310for analyzing user data to identify a best-suited, already trained, machine learning model and dataset. The machine learning development environmet300uses the ML project analysis system310in combination with machine learning models in trained model catalog320and the associated training datasets in the dataset catalog330to analyze user data and search queries, and to identify matching trained machine learning models and datasets that are relevant to a user's model301,303. The ML project analysis system310includes an analysis and index module/service311, a search query module/service312, a matching engine module/service313, a resource ranking module/service314, and an insight/reporting module/service315. As will be appreciated, the ML project analysis system310may include more, less, and/or different services that what is shown inFIG. 3and described herein.

As disclosed, the trained model catalog320as includes a repository of machine learning models Ma-Mi that are trained, respectively, on the datasets Da-Di in the dataset catalog330. In addition, default machine learning models may be included in trained model catalog320as optional machine learning model based on the user's sample data. To assemble the catalogues320,330, expert providers may train machine learning models (e.g., Ma) on specific data (e.g., Da), and offer the trained machine learning model for a fee. As expert providers develop and train new trained models with training datasets, they are stored, respectively, in the trained model catalog320and dataset catalog330for subsequent use by various users. Expert providers may, in turn, may receive compensation when their particular models are utilized by other users. In one embodiment, expert providers begin with a default machine learning model as a baseline and then enhance the default machine learning model by training them using specific topic training data.

Once the trained model catalog320and dataset catalog330are assembled, the analysis and index module/service311in the ML project analysis system310analyzes and indexes the catalogued datasets, models and relevant data to generate indexed ML project metadata. For example, the analysis/index module/service311may apply natural language processing and analytic analysis to the catalogued models Ma-Mi to identify model metadata (e.g., model type, author(s), training data size, training steps, performance metrics, etc.). In addition, the analysis/index module/service311may apply natural language processing and analytic analysis to the catalogued datasets Da-Di to identify which domains, entities and/or data types are included in the datasets.

In beginning work on the user X model301, user X begins by providing sample data X302to the ML project analysis system310that is representative of the type of data used by user X model301. For example, sample data X302may include text beginning with who/what/when/where/why/how which the search query module/service312in the ML project analysis system310evaluates along with user-defined or inferred query parameters to build a search query from specified domain, entities, model type, keywords, etc. relating to the user X model301.

The ML project analysis system310evaluates the sample data X302against the catalogued models and datasets320,330with the matching engine module/service313to determine which categorized models320and datasets330are most relevant to the user X model301. For example, the matching engine module/service313may apply natural language processing and analysis to compare the indexed ML project metadata to the source project's extracted search metadata.

When matching analysis completes, the ML project analysis system310ranks the matching ML models and datasets using the resource ranking module/service314based on the relevancy of the indexed ML project metadata to the user search data301. For example, the resource ranking module/service314may apply user-specified preferences to tune the weight of commonality matches to account for the preferences. For example, if User X301is looking for a Watson Assistant project with similar intents, the resource ranking module/service314may be configured to weigh the matches of intents higher than similarly common utterances, dialog nodes, logs or domains. Or if the User X301is interested in Visual Recognition projects near a certain location in the user's domain, the resource ranking module/service314may be configured to weigh those metadata metrics higher over the accuracy, size, resolution, or classification of an image set.

Once the most relevant ML project resources are identified, the ML project analysis system310returns the most relevant project matches to the search user using the insight/reporting module/service315along with an assigned relevancy score for each resource so that the user can explore each returned project in depth. To this end, the insight/reporting module/service315may be configured to determine a difference metric between the user's content and each returned result. Using the difference metric values, the insight/reporting module/service315makes recommendations which are scaled based on user query preferences and/or generates insights about the user model X.

Likewise, user Y system303provides sample data Y304to the ML project analysis system310and requests ML project resources. The ML project analysis system310evaluates sample data Y304against the trained model catalog320and the associated training datasets in the dataset catalog330using a set of the cognitive services311-315to analyze user data and search queries, and to identify matching trained machine learning models and datasets that are relevant to a user's model303. When testing completes, the ML project analysis system310ranks the ML project resources and dynamically links them to the user Y model303.

To provide additional details for an improved understanding of selected embodiments of the present disclosure, reference is now made toFIG. 4which is a diagrammatical depiction of a cloud-based system400for sharing and selecting catalogued ML project resources420,430with one or more user models401,402using a ML project analysis system410by analyzing metadata commonalities between the user data and catalogued models/datasets. As shown inFIG. 4, the ML project analysis system410enhances both the user X model401and the user Y model402by dynamically identifying different machine learning models from trained model catalog420and different datasets from dataset catalog430based on analysis of the user's sample data and/or submitted search requests. For each ML project411,412(e.g., Project 1), the corresponding trained model (e.g., Ma) may be trained on an associated dataset (e.g., Da) and then stored in the catalogs420,430for subsequent reuse without requiring retraining, thus eliminating time and costs to retrain or reconstruct machine learning models. However, based on prior analysis of sample data from the user model and indexed metadata from the catalogued ML project resources420,430, the user models401,402interfacing with the ML project analysis system410may be dynamically linked to best-fitting models and datasets which need not necessarily be part of the same ML project.

To this end, the ML project analysis system410may evaluate sample data and/or search query information from the User X model401against each of the models Ma-Mi and datasets Da-Di in the catalogs420,430to identify matching trained models Md, Mg and datasets Dd, De as being the most relevant matches for the User X model401. In the depicted cloud-based400, the ML project analysis system410is deployed on cloud440with trained model catalog420and dataset catalog430, and applies one or more cognitive and/or NLP services, such as a question and answer service, natural language classifier service, to identify matching trained models Md, Mg and datasets Dd, De which are ranked for relevancy to the user X model401. Cloud400may be a public cloud, a private cloud, a hybrid cloud, or any other type of accessible computer network.

Likewise, ML project analysis system410may evaluate sample data and/or search query information from the User Y model402against each of the models Ma-Mi and datasets Da-Di in the catalogs420,430to identify matching trained models Mc, Me and datasets Dg, Di as being the most relevant matches for the User X model402. As a result, the ML project analysis system410applies one or more cognitive and/or NLP services to identify matching trained models Mc, Me and datasets Dg, Di which are ranked for relevancy to the user Y model402.

To provide additional details for an improved understanding of selected embodiments of the present disclosure, reference is now made toFIG. 5which depicts a simplified flow chart500showing the logic for matching machine learning resources with user search queries. The processing shown inFIG. 5may be performed in whole or in part by a cognitive system, such as the information handing system100or other natural language question answering system, which uses natural language classifier services, NLP services and related analytics analysis to generate indexed ML project resource metatdata510, to perform search query analytics520, to perform search queries to find matching resources530, and to present matching ML resources to the user540in response to the user search query.

In the depicted process flow500, processing commences at501which may occur during design and/or test time when developers assemble a plurality of ML projects having multiple ML models and datasets for storage in a machine learning development environment, such as the Watson Studio system. At this point, the method steps510,520,530,540embody the functionality of a design tool that evaluates user-defined query parameters (e.g., domain, model type, example entities, etc.) and/or inferred parameters from sample data of the user) against submitted ML models and databases from a database to identify matching ML project resources that are relevant to the user. As explained below, the design tool effectively searches the model/dataset database for elements used in building machine learning models that are relevant to the designer's project needs using one or more search and analysis methods described as follows.

Using the assembled ML projects, the existing pertinent data sets, models and relevant data are analyzed and indexed at step510to generate indexed ML project metadata. The generation of indexed metadata at step510may be performed at the information handling system100or ML project analysis system310by applying NLP processing, extraction algorithms, and/or machine learning techniques to the catalogued ML models and datasets from the assembled ML projects. As will be appreciated, one or more processing steps may be employed to generated indexed ML project metadata.

For example, the analysis and index processing at step511may employ NLP processing and classifier services to identify, for each trained ML model, model metadata, such as model type, author(s), training data size, training steps, performance metrics, algorithm type, and the like. In addition or in the alternative, the analysis and index processing at step512may analyze and index identify, for each trained training dataset, training metadata, such as domains and/or entities included in the dataset, data types, and the like. In addition or in the alternative, the analysis and index processing at step513may identify code and/or processing metadata used by the ML model and/or metadata, such as, for example, processing steps to convert images to pixels, to convert all inputs to lower case, to reduce the image size, to convert to gray scale, or the like. As disclosed herein, the processing at steps511-513may be performed at the information handling system100or ML project analysis system310by employing one or more NLP, extraction, and/or machine learning algorithms to search for elements used in building ML models and to generate ML project metadata. Examples of data and metadata that can be indexed for an ML based project, such as Watson Assistant, include utterances, entities, intents, dialog nodes, logs, domains found in the utterances/responses, and the like. Similarly, for a Visual Recognition project, example metadata for indexing would include image sample sets and properties (size, resolution, location data, classification), entities/domains in the image, and the like. In addition, a Watson Studio project could be analyzed to index project metadata, such as entities/domain in training columns, extracted features, model algorithms, and the like.

Once indexing of the ML project models and datasets is complete, a search query may be built at step520based on search data or parameters provided by the user. As disclosed herein, the search query may be built at the information handling system100or ML project analysis system310by employing one or more NLP, extraction, and/or machine learning algorithms which are applied to the sample data received from a user to extract search metadata. Examples of search metadata that can be extracted from the sample data include user-defined query parameters, such as domain, model type, example entities, and the like. In addition or in the alternative, the processing at step520may be applied to infer parameters by analyzing the sample data to extract domain, entities, keywords, and the like. In addition or in the alternative, the processing at step520may allow the user to provide parameters for use in ranking or scoring the search results as part of the search query. Though selected embodiments of the present disclosure may use a user search query for related models at step520to trigger the subsequent project resource recommendation steps530-540, it will be appreciated that the ML project resource search functionality may be triggered to other activities. For example, the project resource search functionality may be triggered by an upload action from the user (e.g., a git commit hook or Watson Studio action) for certain data types (csv, etc) which prompts the system to proactively make recommendations as described hereinbelow.

Once the search query is built, the search query is performed at processing step530to find the matching ML project resources. As disclosed herein, the search query processing at step530may be performed at the information handling system100or ML project analysis system310to find ML projects from the database that are similar to the catalogued models and datasets based on the indexed ML project metadata. As will be appreciated, one or more processing steps may be employed to perform the search query.

For example, the search query processing at step531may employ NLP processing and classifier services to compare indexed metadata for each trained ML project with search metadata. In addition or in the alternative, the search query processing at step532may analyze and identify metadata commonalities between the search metadata and project metadata. Examples of using metadata commonalities to determine the best matches for an ML based project, such as Watson Assistant, include identifying commonalities between utterances, entities, intents, dialog nodes, and the domains/entities in the search query. Similarly, for a Visual Recognition project, example search techniques would include identifying commonalities between domain/entities in training data sets and the domain/entities in the query. In addition, a Watson Studio project could be analyzed to find commonalities between entities/domain in training columns and the domain/entities in the query, as well as models of the query-specified type. In addition or in the alternative, the search query processing at step533may tune the results based on user-specified preference rankings. Examples of tuning the weight of commonality matches to account for user preferences for an ML based project, such as Watson Assistant with similar intents, might include weighing the matches of intents higher than similarly common utterances, dialog nodes, logs or domains. Alternatively, if the user preference is to enhance the extracted features for a machine learning model, such as one built in Watson Studio, the tuning might find Watson Knowledge Studio entity data of the same domain valuable as augmented features (WKS Bigrams or entity Co-location data). As disclosed herein, the processing at steps531-533may be performed at the information handling system100or ML project analysis system310by employing one or more NLP, extraction, and/or machine learning algorithms to find project resources that match the search query.

Once search results are returned, the matching ML resources are presented to the user at processing step540to assist the user with expediting construction of a data science project. As disclosed herein, the presentation of matching resources at step540may be performed at the information handling system100or ML project analysis system310to identify ML project resources which are in the same or similar domain, which use machine learning models or ground truth that would expedite development, which use techniques that could apply to the user's project, or otherwise would help bootstrap the user's project. As will be appreciated, one or more processing steps may be employed to perform the search query.

An example of a resource presentation processing step540is to employ NLP and interface display processing at step541to return the most relevant ML project resources to the search user with an assigned relevance score for each resource and/or a summary of the characteristics of the matching/recommended ML project resources (i.e., dataset sizes). The interface display processing step541can provide relevant links to training data, models, model authors, etc. With this information, the user can explore each returned project resource in depth.

In addition or in the alternative, the resource presentation processing at step542may evaluate the differences between the user model being developed and the returned ML resources. For example, the evaluation processing step542may determine a difference metric between the user's content and each returned result, such as noting a difference between the number of dataset images (or image format) in a ML project resource and the number of dataset images (or image format) in the user's model/dataset.

In addition or in the alternative, the resource presentation processing at step543may generate recommendations and/or insights for the user based on the identified difference metric values. For example, the difference metrics can be used to prioritize the presentation of matching project resources according to the highest variance between the user's model/dataset and the matching model/datasets. In addition, the difference metrics can be used at the resource presentation processing step543to identify and generate recommendations for the user's project to expand the number of entities in the user's model to include entities from resource models with similar domains. In addition, the insight/recommendation processing step543may use the difference metrics to identify and generate, for each matching domain, a list of entities found in that domain per model, where the list is used to make a recommendation of entities to add to the user's model. The simplest implementation is to take the superset of this list and present the list to the user. Alternatively, the list of entities can also be prioritized with a presentation order giving prioritized presentation for entities that appeared in the most different models, for entities that appeared most frequently in proportion to other entities in the domain in the models, or for a combination of the above.

Another example recommendation from the insight/recommendation processing step543would be to tune the user's dataset to better match the dataset(s) in a ML project resource. For example, if the ML project resources include color images and the user dataset includes black and white images, then the different metrics are used to recommended tuning the user dataset to add color images. Or if the user's model uses long-form text, but the models in the other ML project resources use tweets, then the recommended tuning would be to add tweets to the user's model. Additional examples of recommendations for the user's project may include generating, for every matching ML project model with overlapping type characteristics, a histogram of the model features and plot where the current model sits. For instance, with image classification models, histograms could be generated for the image size in pixels, image brightness, image color depth, and the like. And for text-based models, histograms could be generated for the text length, reading level, language, and the like. In addition, histograms can be generated made for number of training data samples. By displaying the histograms and average data values, the user can better understand the variance between the user's model/datasets and each returned project resource.

Another example recommendation from the insight/recommendation processing step543would be to recommend that the user's dataset include processing steps that are done on datasets within the matching model type, such as by identifying or surfacing calls to known functions/APIs. For example, the different metrics can be used to recommend a processing step with the “image_to_grayscale” function (to convert dataset images from color to grayscale) or with the “text_remove_stop_words” function (to recommend a processing step to remove garbage words from the user dataset). In addition or in the alternative, the insight/recommendation processing step543may generate a recommendation which presents the user with the top N functions used in the highest number of models related to your domain.

As disclosed herein, the processing at steps541-543may be performed at the information handling system100or ML project analysis system310by employing one or more NLP, extraction, and/or machine learning algorithms to generate insights and recommendations based on the project resources that match the search query.

To provide additional details for an improved understanding of selected embodiments of the present disclosure, an example is now provided to show how a user can deploy the ML project resource locator functionality to build an auto repair image classification model. In this example, the user may start with a dataset of six images organized into folders of “needs repair” or “runs fine.” Based on a representative sample of the user's dataset, the ML project resource locator infers that the user is working on an “image classification” model type, and may also identify other model-related search metadata (e.g., model algorithm, performance score, etc.). In addition, the ML project resource locator determines that the user dataset is in the “car” domain and includes a set of entities (e.g., automobile/car/truck, Chevrolet/Ford/Buick, etc.), thought other dataset-related search metadata (e.g., data type, characteristics, etc.) may be identified. Using this information to build a search query, the ML project resource locator begins searching for existing models in a database of ML projects. Based on a comparison of the search metadata to the indexed ML project metadata, the ML project resource locator returns three related models/data sets as search results. The first search result Model 1 is an automobile image classification model (antique vs current cars). The second search result Model 2 is an automobile crash reports analysis (text model). The third search result Model 3 is a Self-Driving car model, images about cars, pedestrians, etc. By way of providing an illustrative example, the search results may be provided with one or more indexed model, dataset, and processing step parameters for each model, as shown below:

In addition to providing the model/dataset search results (Model 1, Model 2, Model 3), the ML project resource locator may provide one or more insights to the user based on the search results. In this example, the first insight for the user is that the image classification models in automobile domain all use at least 100 images of at least 1GB each, thereby indicating that the user's dataset needs improvement in the form of additional images and more megapixels. In addition, a second insight may be provided to the user to indicate that images from Model 1 or Model 3 may be candidate ground truths. A third insight may be that the user dataset has six automobile models while Model 2 (the crash reports model) has twelve automobile models, and therefore the user dataset needs a greater variety of automobile images. A fourth insight may be that the images from Model 3 include a gray-scaling step, and therefore this gray-scaling step may be useful for the user's model.

By now, it will be appreciated that there is disclosed herein a system, method, apparatus, and computer program product for recommending machine learning (ML) project resources for a user project with an information handling system having a processor and a memory. As disclosed, the system, method, apparatus, and computer program product ingests a plurality of ML projects comprising a plurality of trained models and/or training datasets. By applying natural language processing (NLP) and analytic analysis to the ML projects, each ML project is categorized with indexed project metadata characterizing the domain(s), entities, and relationships between the entities. In addition, the system applies NLP and analytic analysis to a search request for ML project resources to categorize the request with search metadata characterizing the search keywords, intents, and domain. The system also evaluates the search metadata against the indexed project metadata for each ML project to form a relevancy assessment to the search request for each ML project. In selected embodiments, the relevancy assessment is based on entity matching in a common domain for the search request and each ML project, and may be generated using one or more machine learning algorithms. For example, a first machine learning algorithm may be used to generate a relevancy assessment for a first domain, and second, different machine learning algorithm may be used to generate a relevancy assessment for a second domain. After ordering the plurality of trained models based on the relevancy assessment for each ML project, the system displays one or more recommended ML project resources comprising one or more of the plurality of trained models having a relevancy assessment exceeding a relevancy threshold. In addition, the system may compute a difference metric between model/dataset information extracted each recommended ML project resource and model/dataset information extracted from the user project. Based on the difference metric for each recommended ML project resource, the system may generate an insight recommendation to help improve or finish the user project. Finally, the system may present one or more recommended ML project resources on a user interface (UI) by displaying a representation of each trained model exceeding a relevancy threshold, a corresponding relevancy assessment, and keyword mapping to allow a user to adjust prioritization of recommended ML project resources.

In response to receiving a user entry at the interface to adjust prioritization of recommended ML project resources; the system may update the one or more recommended ML project resources presented on the user interface in response to the user entry.