
Official Google Cloud Certified Professional Machine Learning Engineer Study Guide
- English
- ePUB (mobile friendly)
- Available on iOS & Android
Official Google Cloud Certified Professional Machine Learning Engineer Study Guide
About this book
Expert, guidance for the Google Cloud Machine Learning certification exam
In Google Cloud Certified Professional Machine Learning Study Guide, a team of accomplished artificial intelligence (AI) and machine learning (ML) specialists delivers an expert roadmap to AI and ML on the Google Cloud Platform based on new exam curriculum. With Sybex, you'll prepare faster and smarter for the Google Cloud Certified Professional Machine Learning Engineer exam and get ready to hit the ground running on your first day at your new job as an ML engineer.
The book walks readers through the machine learning process from start to finish, starting with data, feature engineering, model training, and deployment on Google Cloud. It also discusses best practices on when to pick a custom model vs AutoML or pretrained models with Vertex AI platform. All technologies such as Tensorflow, Kubeflow, and Vertex AI are presented by way of real-world scenarios to help you apply the theory to practical examples and show you how IT professionals design, build, and operate secure ML cloud environments.
The book also shows you how to:
- Frame ML problems and architect ML solutions from scratch
- Banish test anxiety by verifying and checking your progress with built-in self-assessments and other practical tools
- Use the Sybex online practice environment, complete with practice questions and explanations, a glossary, objective maps, and flash cards
A can't-miss resource for everyone preparing for the Google Cloud Certified Professional Machine Learning certification exam, or for a new career in ML powered by the Google Cloud Platform, this Sybex Study Guide has everything you need to take the next step in your career.
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Information
Table of contents
- Cover
- Table of Contents
- Title Page
- Copyright
- Dedication
- Acknowledgments
- About the Author
- About the Technical Editors
- Introduction
- 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
- Index
- End User License Agreement