Abstract: Machine learning model training is provided. A model training result of a machine learning model is predicted utilizing a classification model based on a plurality of different combinations of input data set properties, settings of the machine learning model, and machine learning model training environment properties. Model training duration of the machine learning model is predicted utilizing a regression model based on those combinations that had a predicted successful model training result. Capacity unit hours is determined for each respective combination having the predicted successful model training result based on a corresponding predicted model training duration of the machine learning model. A particular combination of input data set properties, settings of the machine learning model, and machine learning model training environment properties that has minimum capacity unit hours is selected. The machine learning model is trained using the particular combination.

Abstract: Embodiments of the present invention disclose a method, computer program product, and system for estimating the results of a performance test on an updated software application. A method, the method comprising receiving an updated software application, wherein the size of the updated software application is a first size and generating a plurality of small probe, wherein the size of each of the small probe data is a second size, wherein the second size is less than the first size. Conducting a first performance test on the plurality of small probe data and calculating an estimated elapsed time for a performance test on the updated software application. Conducting the performance test on the updated software application and determining if the updated software is given a PASS or FAIL for the performance test, based in part on the elapsed time of the performance test on the updated software application.

Abstract: A method, system, and computer program product for self-learning reference mechanisms for model selection in AutoAI. The method identifies a set of data summary statistics within a data set. A data pattern group is identified within the set of data summary statistics. The data pattern group is determined to be mature. A model selection acceleration mechanism (MSAM) model is generated based on the data pattern group. The method predicts a set of top-k models for the data set based on the MSAM model.

Abstract: Described are techniques for machine learning library management. The techniques include generating a table including a plurality of machine learning libraries and their current versions that are used in a deployed machine learning platform (MLP) instance, a first available version upgrade for a first machine learning library of the plurality of machine learning libraries, a security indication associated with the first available version upgrade relative to a current version implemented by the first machine learning library, and a compatibility indication between the first available version upgrade and the current version of the first machine learning library. The techniques further include generating a recommendation related to upgrading the first machine learning library based on the security indication and the compatibility indication.

Abstract: Described are techniques for machine learning library management. The techniques include generating a table including a plurality of machine learning libraries and their current versions that are used in a deployed machine learning platform (MLP) instance, a first available version upgrade for a first machine learning library of the plurality of machine learning libraries, a security indication associated with the first available version upgrade relative to a current version implemented by the first machine learning library, and a compatibility indication between the first available version upgrade and the current version of the first machine learning library. The techniques further include generating a recommendation related to upgrading the first machine learning library based on the security indication and the compatibility indication.

Abstract: A computer-implemented system, platform, programing product, and/or method for improving transformation selection in an ensemble machine learning (ML) model that includes: providing all base ML models of the ensemble ML model; identifying all of a plurality of Derived Fields in all the base ML models; performing a Derived Field run prediction analysis for all the Derived Fields; computing the Derived Field Importance Weight for Field (DFIW4F) and the Derived Field Importance Weight for Model (DFIW4M) for all the Derived Fields; clustering all the Derived Fields into a plurality of Derived Field clusters, wherein each Derived Field cluster is based upon the DFIW4M and the DFIW4F for the Derived Field; sorting all the Derived Field clusters by best cluster based upon DFIW4M and DFIW4F; and running the base ML models based upon the Derived Fields in the best Derived Field cluster until sufficient base ML models have been run.

Abstract: Techniques for optimizing machine learning models are provided. A first model in a first format is received. A second model is generated by applying one or more optimization techniques to the first model; the second model is an optimized version of the first model. The second model is converted into a common intermediate format. The second model is converted into binary data representing the second model. The binary data representing the second model is outputted.

Abstract: One or more computer processors calculate a cache prediction for a received inference request within an inference cache structured as a self-learning tree, wherein the inference request comprises a set of input values. The one or more computer processors responsive to the retrieved cache prediction exceeding a cache prediction threshold, transmit the cache prediction. The one or more computer processors parallel compute a model prediction for the received inference request utilizing a trained model. The one or more computer processors responsive to the retrieved model prediction exceeding a model prediction threshold, convert the trained model into a tree structure. The one or more computer processors update the inference cache with the converted train model. The one or more computer processors transmit the model prediction.

Abstract: Systems and computer-implemented methods select a subset of methods to generate data schemas for input data from a list of methods for generating data schemas, based on output of a regression model; generate a candidate schema for each method in the subset of methods to generate data schemas; and generate a master data schema for the input data by merging the candidate schema for each method in the subset of methods to generate data schemas, utilizing predetermined rules.

Abstract: A method for instantiating a first specific model, as one record, based on a model scoring template, clustering a plurality of models into a plurality of groups to obtain a plurality of clusters, computing a model contribution weight value for the given cluster, choosing a set of top model(s) from the plurality of models, and updating the model(s) of the set of top model(s) dynamically.

Abstract: Methods, computer program products, and systems are presented. The method, computer program products, and systems can include, for instance: examining an enterprise dataset, the enterprise dataset defined by enterprise collected data; selecting one or more synthetic dataset in dependence on the examining, the one or more synthetic dataset including data other than data collected by the enterprise; training a set of predictive models using data of the one or more synthetic dataset to provide a set of trained predictive models; testing the set of trained predictive models with use of holdout data of the one or more synthetic dataset; and presenting prompting data on a displayed user interface of a developer user in dependence on result data resulting from the testing, the prompting data prompting the developer user to direct action with respect to one or more model of the set of predictive models.

Abstract: Embodiments of the present invention disclose a method, computer program product, and system for estimating the results of a performance test on an updated software application. A method, the method comprising receiving an updated software application, wherein the size of the updated software application is a first size and generating a plurality of small probe, wherein the size of each of the small probe data is a second size, wherein the second size is less than the first size. Conducting a first performance test on the plurality of small probe data and calculating an estimated elapsed time for a performance test on the updated software application. Conducting the performance test on the updated software application and determining if the updated software is given a PASS or FAIL for the performance test, based in part on the elapsed time of the performance test on the updated software application.