Google Cloud PMLE 30-, 60-, and 90-day study plan for training, serving, MLOps, review loops, and final-week priorities.
This plan is a compact route for Google Cloud Professional Machine Learning Engineer (PMLE). It assumes you are using TechExamLexicon for concept clarity and the exact IT Mastery page for practice routing.
| Day | Focus | What to do |
|---|---|---|
| Day 1 | Orientation and scope | Read the exam guide overview and official vendor page, then use this study plan to mark the lanes you already know and the lanes that need practice. |
| Day 2 | Problem framing and data prep | Choose features, labels, splits, evaluation metrics, and responsible data handling. |
| Day 3 | Model development | Use AutoML, custom training, notebooks, pipelines, and experiment tracking appropriately. |
| Day 4 | Deployment and serving | Pick online, batch, endpoint, versioning, scaling, and rollback patterns. |
| Day 5 | MLOps and monitoring | Track drift, skew, quality, explainability, lineage, and retraining triggers. |
| Day 6 | GenAI and Vertex AI | Apply model selection, prompt management, grounding, safety, and evaluation when generative AI appears. |
| Day 7 | Timed review and scheduling decision | Run a timed practice block, review explanations, update a one-page rule sheet, and verify current vendor facts before scheduling. |