Hands-on labs and notes for the Google Cloud Professional Machine Learning Engineer certification.
| Lab | Topic | Status |
|---|---|---|
| Lab 1 | Feature Engineering with BigQuery ML | ✅ Complete |
| Lab 2 | End-to-End Pipeline in Vertex AI (AutoML + Custom Training + Serving) | ✅ Complete |
| Lab 3 | Hyperparameter Tuning with Vertex AI Vizier | ✅ Complete |
| Lab 4 | Model Monitoring & Drift Detection | ✅ Complete |
| Lab 5 | MLOps Services | ✅ Complete |
| Lab 6 | Vertex AI Agent Builder (RAG) | ✅ Complete |
| Lab 7 | Text Classification with Stack Overflow Data | ✅ Complete |
| Lab 8 | Image Classification with Satellite Data | ✅ Complete |
| Lab 9 | Time Series Forecasting | ✅ Complete |
| Lab 10 | Vertex AI Pipelines | ✅ Complete |
| Mini-Lab A | Custom Prediction Routine (CPR) | ✅ Complete |
| Mini-Lab B | Explainability (Sampled Shapley) | ✅ Complete |
| Mini-Lab C | TFRecord Pipeline | ✅ Complete |
| Mini-Lab D | Shadow Deployment | ✅ Complete |
| Mini-Lab E | Dataflow + RunInference | ✅ Complete |
See ml_labs/ml_labs_plan.md for the full study plan.
ml_crash_course/— Foundational ML notebooks (linear regression, classification, fairness)
python3.13 -m venv .venv
source .venv/bin/activate
pip install jupyter pandas scikit-learn google-cloud-aiplatformCost tip: Delete Vertex AI endpoints, models, and monitoring jobs after each lab session. Run
gcloud ai endpoints list --region=us-central1to audit running resources.