Most ML projects never leave Jupyter. The gap between experimentation and production is where value gets lost. MLOps bridges that gap.

We explore:

  • Model versioning and registry
  • Automated retraining pipelines
  • Monitoring for data and model drift
  • Canary deployments for ML

Production ML is as much about operations as it is about algorithms.