Hewlett Packard Enterprise Development LP

Sagar Venkappa Nyamagouda

Prasad Pimplaskar

Jayasankar Nallasamy

Chinmay Chaturvedi

Chirag Talreja

Kalapriya Kannan

Anand Andaneppa Ganjihal

Rachit Gupta

Smitha Jayaram

Sherine Jacob

Hiro Rameshlal Lalwani

Systems and methods are provided for generating extract-transform-load ('ETL') machine learning (“ML”) pipeline validation rules based on user-input, wherein the ETL ML pipeline validation rules may be applicable to validate an ETL ML pipeline against multiple test datasets. The ETL ML pipeline validation rules may comprise compute-type validation rules for computing expected values of data structures within a dataset output by the ETL ML pipeline. The ETL ML pipeline validation rules may comprise check-type validation rules for checking whether data structures within a dataset output by the ETL ML pipeline have intended characteristics.

Data correctness and validation using validation definition language

In some examples, a system receives workload information of a workload collection, and applies a machine learning model on the workload information, the machine learning model trained using training information including features of different types of workloads. The system produces, by the machine learning model, an identification of a first file system from among different types of file systems, the machine learning model producing an output value corresponding to the first file system that is a candidate for use in storing files of the workload collection.

File system provisioning for workload

Systems and methods are provided for generating extract-transform-load ('ETL') machine learning (“ML”) pipeline validation rules based on user-input, wherein the ETL ML pipeline validation rules may be applicable to validate an ETL ML pipeline against multiple test datasets. The ETL ML pipeline validation rules may comprise compute-type validation rules for computing expected values of data structures within a dataset output by the ETL ML pipeline. The ETL ML pipeline validation rules may comprise check-type validation rules for checking whether data structures within a dataset output by the ETL ML pipeline have intended characteristics. Where the ETL ML pipeline validation rules are applicable to validate an ETL ML pipeline against a test dataset which was not referenced to describe the ETL ML pipeline validation rules, then the ETL ML pipeline may reuse these ETL ML pipeline validation rules to validate the ETL ML pipeline without further user-input.

Automatic expected validation definition generation for data correctness in AI/ML pipelines

Growing community of inventors

Karnataka, India

Sagar Venkappa Nyamagouda