Abstract: Techniques are provided for caching of machine learning model training parameters. One method comprises training a machine learning model using a given training dataset; and caching a parameter of the machine learning model from the training with the given training dataset. The cached parameter of the machine learning model is used for a subsequent training of the machine learning model. The caching may be performed after each of multiple iterations of the training of the machine learning model. A given cached iteration of the training of the machine learning model may be identified using a key based on: (i) a hash of the given training dataset, (ii) a hash of the machine learning model parameter, and/or (iii) hyperparameters of the machine learning model. The caching of a given iteration of the machine learning model may occur when the given cached iteration is not found in a cache memory.

Abstract: Techniques are provided for caching of machine learning model training parameters. One method comprises training a machine learning model using a given training dataset; and caching a parameter of the machine learning model from the training with the given training dataset. The cached parameter of the machine learning model is used for a subsequent training of the machine learning model. The caching may be performed after each of a plurality of iterations of the training of the machine learning model. A given cached iteration of the training of the machine learning model may be identified, for example, using a key based at least in part on: (i) a hash of the given training dataset, (ii) a hash of the machine learning model parameter, and/or (iii) hyperparameters of the machine learning model. The caching of a given iteration of the machine learning model may occur when the given cached iteration is not found in a cache memory.

Abstract: Techniques are provided for analytically generating micro-service Consumer- Driven Contracts and automated tests. One method comprises obtaining a plurality of usage data records for consumers of a service from a run-time environment; extracting data features from the usage data records; applying a clustering algorithm to the usage data records to assign the usage data records to a given usage pattern cluster of a plurality of usage pattern clusters based on the extracted data features, wherein each of the plurality of usage pattern clusters comprises usage data records; and performing the following steps when the clustering algorithm creates a new usage pattern cluster: creating a new Consumer-Driven Contract that defines consumer expectations of the service, with respect to the new usage pattern associated with the new usage pattern cluster; and generating automated Consumer-Driven Contract tests to test the new Consumer-Driven Contract.

Abstract: Techniques are provided for machine learning based query optimization for federated databases. An exemplary method comprises obtaining a query to be processed in a federated database; generating at least one predictive data movement instruction to move data to a target data source when the target data source satisfies one or more of a predefined efficiency criteria with respect to a query type of the query and a predefined capacity criteria at an expected execution time of the query; and generating a query execution plan for the query by calculating a cost of execution for a plurality of potential target data sources and selecting a target data source for the query based on the calculated cost of execution. The federated database optionally employs a dynamic federated query schema.

Abstract: Techniques are provided for generating specifications for a microservice implementation of an existing application. An exemplary method comprises: analyzing request data and corresponding response data for an application implemented as a monolithic application and/or a Service Oriented Architecture application to generate data features; parsing an audit log and/or a transaction log of the application to identify interactions with a data store; clustering the data store interactions using an unsupervised learning technique to identify patterns of usage of the data store; selecting one or more service types to generate using a trained supervised machine learning model for the requests, the corresponding response data and the data store interactions; and generating an application programming interface specification, a data model specification and/or a message specification for the selected service types for a microservice implementation of the application.

Abstract: Techniques are provided for generating specifications for a microservice implementation of an existing application. An exemplary method comprises: analyzing request data and corresponding response data for an application implemented as a monolithic application and/or a Service Oriented Architecture application to generate data features; parsing an audit log and/or a transaction log of the application to identify interactions with a data store; clustering the data store interactions using an unsupervised learning technique to identify patterns of usage of the data store; selecting one or more service types to generate using a trained supervised machine learning model for the requests, the corresponding response data and the data store interactions; and generating an application programming interface specification, a data model specification and/or a message specification for the selected service types for a microservice implementation of the application.