MLflow

MLflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models.

Web site

Github repository

Tech tags:

• Machine learning
• Model governance

Related shared contents:

• coSTAR: How We Ship AI Agents at Databricks Fast, Without Breaking Things
  project
  2026-03-20

  The article discusses the coSTAR methodology developed at Databricks for building and deploying AI agents with a focus on automated testing and refinement. It highlights the transition from a slow, manual review process to a rapid, automated testing framework that significantly reduces the time to verify changes. By using MLflow and a structured approach involving scenario definitions, trace capture, and judge assessments, coSTAR enhances development velocity and confidence in the quality of AI agents. The methodology addresses the unique challenges of testing non-deterministic outputs in AI systems.
• MLOps Best Practices - MLOps Gym: Crawl
  vision
  2025-01-06

  Good summary and topics for ML ops.

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