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GCP ML Engineer study guide

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Lessons

  1. CI/CD/CT for ML with Cloud Build — Free Study Guide 2026
  2. Choosing a Retraining Policy — Free Study Guide 2026
  3. Training Models with Agent Platform AutoML
  4. Building with Document AI, Vision, Translati
  5. Fine-Tuning & Evaluating Gemini Models from
  6. Feature Engineering & Feature Selection in B
  7. Choosing, Training & Predicting with BigQuer
  8. Modeling Techniques for Interpretability Req
  9. Choosing the Model Type: ARIMA, Trees, DNNs
  10. Choosing the Product & Deployment Strategy
  11. Batch vs. Online Inference on Agent Platform
  12. Serving Beyond Agent Platform Endpoints: Mod
  13. Prebuilt & Custom Serving Containers — Free Study Guide 2026
  14. Evaluating Gen AI: Computation Metrics, Rubr
  15. Evaluation Metrics for Predictive Models
  16. From Vertex AI to Gemini Enterprise Agent Pl
  17. PMLE Exam Facts, Policies & the June 2026 Bl
  18. Creating & Consolidating Features in Agent P
  19. Organizing & Exploring Tabular, Text & Image
  20. Protecting PII & Sensitive Data in ML Workfl
  21. Choosing a Preprocessing Tool: BigQuery, Dat
  22. Optimizing Gemini Applications for Cost, Lat
  23. Building & Tuning Solutions with Gemini, Ima
  24. Evaluating & Selecting Models in Model Garde
  25. Monitoring, Testing & Evaluating Gen AI Solu
  26. Configuring Model Monitoring on Agent Platfo
  27. Training-Serving Skew, Data Drift, Concept D
  28. Experiment Environments, Tracking & Lineage
  29. Prototyping with PyTorch, scikit-learn, JAX
  30. Workbench vs. Colab Enterprise: Secure, Coll
  31. Orchestrating ML Workflows with Managed Serv
  32. Ray on Agent Platform for Distributed ML Wor
  33. Agent Platform Pipelines with KFP: Component
  34. Validating Data & Models in Pipelines — Free Study Guide 2026
  35. Ensuring Consistent Preprocessing Between Tr
  36. Serving Features Online with Feature Store
  37. Tuning Models for Production Training & Serv
  38. Public, Dedicated & Private Endpoints — Free Study Guide 2026
  39. Serving Hardware & Autoscaling for Throughpu
  40. Protecting AI Systems from Data & Model Exfi
  41. Model Explainability on Agent Platform — Free Study Guide 2026
  42. Defending Gen AI Apps: Safety Filters, Model
  43. Responsible AI: Detecting & Monitoring Bias
  44. Custom Training on Agent Platform: Container
  45. Kubeflow on GKE, AutoML & Tabular Workflows
  46. Organizing & Ingesting Training Data from Cl
  47. Distributed Training: Data & Model Paralleli
  48. Choosing CPUs, GPUs & TPUs for Training
  49. Fine-Tuning Foundation Models & When to Tune
  50. Hyperparameter Tuning on Agent Platform
  51. Troubleshooting ML Training Failures — Free Study Guide 2026
  52. Inference Preprocessing & Postprocessing
  53. Organizing & Versioning Models in Model Regi
  54. Rollout Strategies: A/B Tests, Canaries & Tr