Machine Learning Lifecycle Management

A system for managing a lifecycle of a machine learning (ML) application from a consumer point of view, the system comprising:

• one or more data processors;
• and
• memory storing instructions stored on at least one of the one or more data processors, which when executed, result in operations comprising:
• initiating execution of an intelligent scenario for training of the ML application;
• generating, by an integrator component, a training pipeline, wherein the training pipeline comprises training logic associated with a defined workflow for the training;
• training, by an application having an input dataset, the ML application using the training pipeline;
• determining, by the integrator component, training metrics associated with the trained ML application, wherein the training metrics are indicators of a level of accuracy of the trained ML application;
• and
• providing, by a centralized component, the training metrics for characterization of the trained model.

A non-transitory computer program product storing instructions which, when executed by at least one data processor forming part of at least one computing device, implement operations comprising:

• initiating execution of an intelligent scenario for training of the ML application;
• generating, by an integrator component, a training pipeline, wherein the training pipeline comprises training logic associated with a defined workflow for the training;
• training, by an application having an input dataset, the ML application using the training pipeline;
• determining, by the integrator component, training metrics associated with the trained ML application, wherein the training metrics are indicators of a level of accuracy of the trained ML application;
• and
• providing, by a centralized component, the training metrics for characterization of the trained model.