About This Study Plan
This 30-day study plan breaks the PMLE (ML Engineer) exam preparation into 4 focused study sessions with 16 actionable tasks. The plan covers all 5 exam domains — Architecting ML Solutions, Data Preparation, Feature Engineering, Training Models, Serving and Scaling — ensuring complete coverage. Structured 30-day plan for the Google Professional Machine Learning Engineer exam.
Prerequisites
- ML basics (model training)
- Python experience
- 2–3 hours per day
Study Schedule
- Days 1–2: ML problem framing and approach selection
- Days 3–4: Feature engineering and Feature Store
- Days 5–6: Data preparation, validation, and BigQuery ML
- Day 7: End-to-end data pipeline for ML training
- Days 8–9: Vertex AI Training — custom and AutoML
- Days 10–11: Hyperparameter tuning and distributed training
- Days 12–13: Model deployment — endpoints, batch, and serving
- Day 14: Model optimization and scaling strategies
- Days 15–16: Vertex AI Pipelines and CI/CD for ML
- Days 17–18: Model monitoring — drift, skew, and retraining triggers
- Days 19–20: Experiment tracking and model registry
- Day 21: Responsible AI — fairness, explainability, and privacy
- Days 22–23: Full practice exam #1 + review
- Days 24–25: MLOps pipeline design scenarios
- Days 26–27: Full practice exam #2 + weak areas
- Days 28–30: Vertex AI service map, flashcards, and rest
Study Tips
Build a complete ML pipeline using Vertex AI Pipelines.
Understand the difference between model monitoring and data monitoring.
Know when to use AutoML vs custom training.
Recommended Google Cloud Study Resources
Supplement this study plan with Google Cloud Skills Boost, which provides structured learning paths aligned to each certification. The Google Cloud documentation is exceptionally detailed and frequently referenced in exam questions. Take advantage of the free $300 credit for new GCP accounts to build real projects during your study period.
Ready to Practice?
Put your study plan into action with ML Engineer practice questions.