A practical ML-ASSOC study plan you can follow: 30-day intensive, 60-day balanced, and 90-day part-time schedules with weekly focus, suggested hours/week, and MLflow-first practice tips.
This page answers the question most candidates actually have: “How do I structure my ML‑ASSOC prep?”
ML‑ASSOC is platform-focused: learn the workflow and make MLflow instincts automatic.
Use the plan that matches your available time, then follow the loop: Syllabus → drills → review misses → mixed sets → timed runs.
| Your starting point | Typical total study time | Best-fit timeline |
|---|---|---|
| You use MLflow and build models on Databricks already | 25–40 hours | 30–60 days |
| You know ML but are newer to Databricks/MLflow | 40–70 hours | 60–90 days |
| You’re new to ML workflows | 70–100+ hours | 90 days |
Target pace: ~8–10 hours/week.
| Week | Focus | What to do | Links |
|---|---|---|---|
| 1 | Data prep + features | Feature engineering on Spark, leakage awareness, splitting strategy. Daily drills + miss log. | Syllabus • Cheatsheet |
| 2 | Training + evaluation | Metrics selection, CV/tuning awareness, interpreting results. Mixed sets mid-week. | Cheatsheet • Practice |
| 3 | MLflow tracking | Runs, params/metrics/artifacts, comparing runs, reproducibility. Make “what to log” automatic. | Syllabus • Practice |
| 4 | Model lifecycle | Registry, versioning, stage transitions, deployment concepts. Finish with timed mixed runs. | Practice • FAQ |
| Weeks | Focus |
|---|---|
| 1–2 | Data prep + features |
| 3–4 | Training + evaluation |
| 5–6 | MLflow tracking + reproducibility |
| 7–8 | Registry + deployment concepts + timed runs |
| Month | Focus |
|---|---|
| 1 | Data prep and evaluation foundations |
| 2 | MLflow tracking and experiment management |
| 3 | Registry + deployment concepts + mixed practice |