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Course Notes: Introduction to MLflow
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    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)