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 |