MLflow

Open-source platform for managing the machine learning lifecycle, offering experiment tracking, model registry and deployment integrations.

MLflow is an open-source platform to manage the end-to-end machine learning lifecycle, including experiment tracking, model packaging, and deployment.

Maturity

Established

Cognitive loadMedium

1UnderstandMeaning & principles
2ContextualizeContext & prerequisites
3WeighValue, limits & risks
4PrepareExamples, steps & metrics

360° overview

MLflow

Six perspectives place the building block in context. The numbers show where each perspective continues in the reading path.

The building block at a glance

MLflow

360°

Classification

2

Complexity: Medium
Impact area: Technical

Technical context

2

Integrations

TensorFlow, PyTorch, scikit-learn

+2

Principles & goals

1

Clear separation of tracking, registry and serving

Value stream stage: Build

Use cases & scenarios

4

Use cases: 3
Scenarios: 3

Compromises

3

Risks: 3
Best practices: 3

I/O & resources

4

Inputs: 3
Outputs: 3