This page answers the question most candidates actually have: “How do I structure my ML‑PRO prep?”
ML‑PRO is production-focused: spend most of your time on lifecycle, governance, and deployment/monitoring decisions.
How long should you study?
| Your starting point | Typical total study time | Best-fit timeline |
|---|
| You deploy models on Databricks already | 35–60 hours | 30–60 days |
| You know ML but are newer to MLOps/governance | 60–100 hours | 60–90 days |
| You’re new to production ML systems | 100–140+ hours | 90 days |
30-Day Intensive Plan
Target pace: ~10–12 hours/week.
| Week | Focus | What to do | Links |
|---|
| 1 | Feature pipelines | Training/serving consistency, leakage prevention, feature lifecycle. | Syllabus • Cheatsheet |
| 2 | Registry + promotion | MLflow runs vs registry versions, approvals, lineage, controlled release. | Cheatsheet • Practice |
| 3 | Deployment patterns | Batch vs online serving, rollout/rollback thinking, testing strategy. | Syllabus • Practice |
| 4 | Monitoring + governance review | Drift, telemetry, retraining triggers, RBAC/lineage mindset. Finish with timed mixed runs. | Practice • FAQ |
60-Day Balanced Plan
| Weeks | Focus |
|---|
| 1–2 | Feature engineering + feature pipelines |
| 3–4 | MLflow registry + release workflows |
| 5–6 | Deployment + testing |
| 7–8 | Monitoring + governance + mixed runs |
90-Day Part-Time Plan
| Month | Focus |
|---|
| 1 | Features + reproducibility foundations |
| 2 | Model lifecycle + deployment patterns |
| 3 | Monitoring + governance + timed runs |