About This Study Plan
This 90-day study plan breaks the PMLE (ML Engineer) exam preparation into 3 focused study sessions with 12 actionable tasks. The plan covers all 5 exam domains — Architecting ML Solutions, Data Preparation, Feature Engineering, Training Models, Serving and Scaling — ensuring complete coverage. Comprehensive 90-day plan for the Google Professional Machine Learning Engineer exam.
Prerequisites
- Basic Python and statistics
- 1–2 hours per day
Study Schedule
- Weeks 1–2: ML basics — supervised, unsupervised, and evaluation metrics
- Week 3: Python ML libraries — scikit-learn, TensorFlow/Keras basics
- Week 4: GCP data services — BigQuery, Cloud Storage, and Dataflow
- End of month: Train a simple model locally and evaluate it
- Week 5: Vertex AI Training — custom jobs, AutoML, and notebooks
- Week 6: Feature engineering, Feature Store, and data pipelines
- Week 7: Model deployment — endpoints, serving, and optimization
- Week 8: Vertex AI Pipelines and MLOps automation
- Week 9: Model monitoring, retraining, and responsible AI
- Week 10: End-to-end ML project on Vertex AI
- Week 11: Practice exam #1 + review
- Week 12: Practice exam #2, pipeline design drills, and final prep
Study Tips
This is one of the hardest GCP exams — allocate significant hands-on time.
Use Vertex AI Workbench for experiments and pipeline development.
Read Google Cloud ML best practices documentation thoroughly.
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.