MLOps applies software engineering and operations practices to machine learning systems. It helps teams move from experimental notebooks to reproducible, monitored and maintainable production solutions.

Machine learning lifecycle

A typical lifecycle includes data preparation, experimentation, training, validation, packaging, deployment, monitoring and retraining.

What MLOps adds

  • Versioned data and models
  • Reproducible training
  • Automated pipelines
  • Model evaluation
  • Deployment automation
  • Production monitoring