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MLflow Learning Roadmap

Experiment tracking, model registry, and reproducible ML pipelines.

Estimated time:2–4 weeks

1🌱

Phase 1 — Fundamentals

Tracking runs (mlflow.log_metric/param)

Tracking server & backend stores

MLflow UI for comparing runs

Autologging for common frameworks

2📦

MLflow Models format (flavors)

Model signatures & input examples

Custom pyfunc models

3🗂️

Registering & versioning models

Stage transitions (staging/production)

Model lineage & metadata

4🚀

important

MLflow Projects for reproducible runs

Serving models (mlflow models serve)

Begin with interview questions to assess your current level.