About This Study Plan

This 7-day study plan breaks the PMLE (ML Engineer) exam preparation into 7 focused study sessions with 28 actionable tasks. The plan covers all 5 exam domains — Architecting ML Solutions, Data Preparation, Feature Engineering, Training Models, Serving and Scaling — ensuring complete coverage. Intensive 7-day review for the Google Professional Machine Learning Engineer exam covering ML pipelines, Vertex AI, model deployment, and MLOps.

7Study Sessions

28Total Tasks

5Domains Covered

Prerequisites

ML fundamentals (supervised/unsupervised)
Python and TensorFlow/PyTorch
5–7 hours per day

Study Schedule

Day 1ML Problem Framing

Translating business problems to ML tasks
Choosing the right ML approach: classification, regression, clustering, recommendation
Data requirements: quality, quantity, and labeling strategies
When NOT to use ML — rule-based alternatives

Day 2Data Engineering for ML

Feature engineering: numerical, categorical, text, and temporal
Feature Store: creation, serving, and versioning
Data validation: TFDV, schema, anomaly detection
BigQuery ML vs Vertex AI: when to use each

Day 3Model Training

Vertex AI Training: custom containers and pre-built algorithms
AutoML: tables, vision, text, and video
Hyperparameter tuning: Vizier and search strategies
Distributed training and GPU/TPU selection

Day 4Model Deployment & Serving

Vertex AI Endpoints: online prediction vs batch prediction
Model versioning, traffic splitting, and A/B testing
Optimization: quantization, distillation, and TF Lite
Scaling: autoscaling, GPU endpoints, and latency optimization

Day 5MLOps & Monitoring

Vertex AI Pipelines (Kubeflow): components, DAGs, and scheduling
Model monitoring: drift detection, skew, and data quality
Continuous training: triggers, retraining strategies, and automation
Experiment tracking: Vertex AI Experiments and metadata

Day 6Practice Exam

Take a full practice exam
Review all incorrect answers
Focus on MLOps and pipeline scenarios
Review model selection and serving questions

Day 7Final Review

Vertex AI service map
ML pipeline architecture reference
Model monitoring checklist
Rest before exam

Study Tips

💡

This exam tests ML engineering, not ML theory — focus on productionization.

💡

Know Vertex AI services: Training, Pipelines, Feature Store, Model Monitoring, Endpoints.

💡

MLOps patterns (CI/CD for ML) are heavily tested.

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.