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GCP ML Engineer study guide

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Lessons

  1. AutoML on Agent Platform: Tabular, Image, Te
  2. Fine-Tuning Gemini Models from BigQuery
  3. BigQuery ML: Model Types, SQL Syntax and In-
  4. Feature Engineering and Feature Selection in
  5. Generating Predictions with BigQuery ML
  6. Optimizing Gemini Applications for Cost, Lat
  7. Generative Media and Models as a Service: Ge
  8. Model Garden: Evaluating and Selecting the R
  9. Industry-Specific AI APIs: Document AI, Visi
  10. Choosing the Preprocessing Tool: BigQuery, D
  11. Data Privacy, PII Handling and Sensitive Inf
  12. Evaluating Generative Solutions: LLM-as-a-Ju
  13. Choosing the Development and Experimentation
  14. Creating and Consolidating Features in Agent
  15. Prototyping with Model Garden Foundational a
  16. Notebook Environments: Collaboration and Sec
  17. Organizing and Exploring Tabular, Text, Imag
  18. Experiment Tracking, Artifact Lineage and Mo
  19. Prototyping in Workbench and Colab Enterpris
  20. Choosing the Deployment Strategy at Design T
  21. Choosing the Model Type: ARIMA, DNN, LLM and
  22. Choosing the Product: AutoML, BigQuery ML, C
  23. Evaluating Compute and Accelerator Options:
  24. Distributed Training Strategies, Hardware Ac
  25. Training SDKs and Services: Custom Training,
  26. Hyperparameter Tuning, Bayesian Optimization
  27. Ingesting Structured and Unstructured Data i
  28. Modeling Techniques for Interpretability Req
  29. Model Evaluation Metrics, Transfer Learning
  30. Organizing Training Data on Cloud Storage an
  31. Troubleshooting ML Model Training Failures
  32. Rollout Strategies: Canary, Traffic Splittin
  33. Tuning Models for Production: Vector Search
  34. Batch and Online Inference: Agent Platform,
  35. Deploying Models to Public and Private Endpo
  36. Scaling the Serving Backend for Throughput
  37. Choosing Serving Hardware: CPU, GPU, TPU and
  38. MLOps CI/CD Automation, Retraining Triggers
  39. Reusable Pipeline Components, Artifacts and
  40. Agent Platform Pipelines: Kubeflow Pipelines
  41. Ray on Agent Platform for Distributed Python
  42. Validating Data and Models Inside Pipelines
  43. Monitoring, Testing and Evaluating Generativ
  44. Model Monitoring: Feature Skew and Drift Det
  45. Responsible AI, Explainable AI, Fairness Met
  46. Securing AI Systems: Exfiltration, Malicious
  47. Operational Troubleshooting, Performance Pro
  48. Exam Overview, Blueprint & Certification Log
  49. Study Strategy, Scenario Mechanics & 8-Week