Machine Learning (ML) Model Management Platform

An Machine Learning (ML) Model Management Platform is a IT platform that can support the creation of ML model management systems.

• Context:
  • It can (often) be a part of an Machine Learning (ML) Platform.
  • It can be created using an ML Model Management Framework.
  • …
• Example(s):
  • MLflow Platform.
  • Sony PlayStation's ML Model Management Platform.
  • DVC Framework.
  • …
• Counter-Example(s):
  • a Feature Store Platform.
• See: Seldon Framework, AWS SageMaker, AWS SageMaker Model Monitor.

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References

2020

• https://mlflow.org/docs/latest/index.html
  • QUOTE: MLflow is an open source platform for managing the end-to-end machine learning lifecycle. It tackles four primary functions:
    • Tracking experiments to record and compare parameters and results (MLflow Tracking).
    • Packaging ML code in a reusable, reproducible form in order to share with other data scientists or transfer to production (MLflow Projects).
    • Managing and deploying models from a variety of ML libraries to a variety of model serving and inference platforms (MLflow Models).
    • Providing a central model store to collaboratively manage the full lifecycle of an MLflow Model, including model versioning, stage transitions, and annotations (MLflow Model Registry).
  • MLflow is library-agnostic. You can use it with any machine learning library, and in any programming language, since all functions are accessible through a REST API and CLI. For convenience, the project also includes a Python API, R API, and Java API.

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