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MLOps Maturity

Updated May 12, 2023 ·

Overview​

MLOps maturity measures automation, collaboration, and monitoring in machine learning processes. It shows areas where improvements can be made.

  • MLOps maturity improves process efficiency.
  • Design phase can't be fully automated, but templates can help.

Levels of MLOps Maturity​

MLOps maturity is divided into three levels, each with different automation and collaboration.

  • Level 1: Manual processes

    • No automation; teams work separately.
    • Development and deployment are done manually.
    • Minimal tracking of features or performance.
    • Slow development and deployment, especially when issues arise.

  • Level 2: Automated development

    • There's some collaboration between teams.
    • Some automation in development (e.g., feature stores, training).
    • Models are developed automatically, but deployment is still manual.
    • Traceability during development, monitoring after deployment.

  • Level 3: Automated development and deployment

    • Close collaboration between teams.
    • Full automation for both development and deployment.
    • CI/CD pipelines for development, testing, and deployment.
    • Models are actively monitored in production.
    • Models may retrain automatically when needed.