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Course Notes

Use this workspace to take notes, store code snippets, or build your own interactive cheatsheet! For courses that use data, the datasets will be available in the datasets folder.

Take Notes

Add notes here about the concepts you've learned and code cells with code you want to keep.

Add your notes here

Tips on MLflow

Tracking (Logging & Monitoring)

  • Set experiments, tags and stuff
  • Use mlrun with info
  • how to use log_metric/s log_param/s and log_artifact/s
  • Search experiments via queries

Models (Packaging & Serving)

  • Flavors (just import mlflow.<target_flavor>)
  • Autolog
  • MLmodel file with configs to export package
  • API's
    • Saving models
    • Logging models
    • Loading models (it uses run_id)
      • last_active_run
      • info
      • run_id
  • Custom Flavor for edge cases
  • MLflow.evaluate()?
  • mlflow models serve -m runs:/{run_id}/model ex.: curl -d '{"dataframe_split": {"columns": ["x"], "data": [[10]]}}' -H 'Content-Type: application/json' -X POST localhost:8080/invocations

Model Registry ()

  • models URI
    • models/
    • models/name/version
    • models/stage (use it with flavors)