Official Google Cloud Certified Professional Machine Learning Engineer study guide / Mona Mona, Pratap Ramamurthy
Material type:
- text
- unmediated
- volume
- 9781119944461
- 004.6782
Item type | Current library | Collection | Call number | Copy number | Status | Barcode | |
---|---|---|---|---|---|---|---|
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Lagro Branch Reference Section | Reference | R 004.6782 M734o 2024 (Browse shelf(Opens below)) | c. 2 | Room use only | 57732QC | |
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Main Library Reference Section | Reference | R 004.6782 M734o 2024 (Browse shelf(Opens below)) | c. 1 | Room use only | 57731QC |
Includes index.
Chapter 1. Framing ML problems
Chapter 2. Exploring data and building data pipelines
Chapter 3. Feature engineering
Chapter 4. Choosing the right ML infrastructure
Chapter 5. Architecting ML solutions
Chapter 6. Building secure ML pipelines
Chapter 7. Model building
Chapter 8. Model training and hyperparameter tuning
Chapter 9. Model explainability on Vertex AI
Chapter 10. Scaling models in production
Chapter 11. Designing ML training pipelines
Chapter 12. Model monitoring, tracking, and auditing metadata
Chapter 13. Maintaining ML solutions
Chapter 14. BigQuery ML
Appendix. Answers to review questions
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