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Official Google Cloud Certified Professional Machine Learning Engineer study guide / Mona Mona, Pratap Ramamurthy

By: Contributor(s): Material type: TextTextSeries: Sybex study guide seriesPublisher: Hoboken, New Jersey : John Wiley & Sons, [2024]Description: xl, 327 pages : illustrationsContent type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
ISBN:
  • 9781119944461
Subject(s): DDC classification:
  • 004.6782
Contents:
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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Holdings
Item type Current library Collection Call number Copy number Status Barcode
Book Book Lagro Branch Reference Section Reference R 004.6782 M734o 2024 (Browse shelf(Opens below)) c. 2 Room use only 57732QC
Book Book 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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