Official Google Cloud Certified Professional Machine Learning Engineer Study Guide

Official Google Cloud Certified Professional Machine Learning Engineer Study Guide

by Mona Mona, Pratap Ramamurthy
Released November 2023
Publisher(s): Wiley
ISBN: 9781119944461

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Book description

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.

Table of contents

  1. Cover
  2. Table of Contents
  3. Title Page
  4. Copyright
  5. Dedication
  6. Acknowledgments
  7. About the Author
  8. About the Technical Editors
    1. About the Technical Proofreader
    2. Google Technical Reviewer
  9. Introduction
    1. Google Cloud Professional Machine Learning Engineer Certification
    2. Who Should Buy This Book
    3. How This Book Is Organized
    4. Bonus Digital Contents
    5. Conventions Used in This Book
    6. Google Cloud Professional ML Engineer Objective Map
    7. How to Contact the Publisher
    8. Assessment Test
    9. Answers to Assessment Test
  10. Chapter 1: Framing ML Problems
    1. Translating Business Use Cases
    2. Machine Learning Approaches
    3. ML Success Metrics
    4. Responsible AI Practices
    5. Summary
    6. Exam Essentials
    7. Review Questions
  11. Chapter 2: Exploring Data and Building Data Pipelines
    1. Visualization
    2. Statistics Fundamentals
    3. Data Quality and Reliability
    4. Establishing Data Constraints
    5. Running TFDV on Google Cloud Platform
    6. Organizing and Optimizing Training Datasets
    7. Handling Missing Data
    8. Data Leakage
    9. Summary
    10. Exam Essentials
    11. Review Questions
  12. Chapter 3: Feature Engineering
    1. Consistent Data Preprocessing
    2. Encoding Structured Data Types
    3. Class Imbalance
    4. Feature Crosses
    5. TensorFlow Transform
    6. GCP Data and ETL Tools
    7. Summary
    8. Exam Essentials
    9. Review Questions
  13. Chapter 4: Choosing the Right ML Infrastructure
    1. Pretrained vs. AutoML vs. Custom Models
    2. Pretrained Models
    3. AutoML
    4. Custom Training
    5. Provisioning for Predictions
    6. Summary
    7. Exam Essentials
    8. Review Questions
  14. Chapter 5: Architecting ML Solutions
    1. Designing Reliable, Scalable, and Highly Available ML Solutions
    2. Choosing an Appropriate ML Service
    3. Data Collection and Data Management
    4. Automation and Orchestration
    5. Serving
    6. Summary
    7. Exam Essentials
    8. Review Questions
  15. Chapter 6: Building Secure ML Pipelines
    1. Building Secure ML Systems
    2. Identity and Access Management
    3. Privacy Implications of Data Usage and Collection
    4. Summary
    5. Exam Essentials
    6. Review Questions
  16. Chapter 7: Model Building
    1. Choice of Framework and Model Parallelism
    2. Modeling Techniques
    3. Transfer Learning
    4. Semi‐supervised Learning
    5. Data Augmentation
    6. Model Generalization and Strategies to Handle Overfitting and Underfitting
    7. Summary
    8. Exam Essentials
    9. Review Questions
  17. Chapter 8: Model Training and Hyperparameter Tuning
    1. Ingestion of Various File Types into Training
    2. Developing Models in Vertex AI Workbench by Using Common Frameworks
    3. Training a Model as a Job in Different Environments
    4. Hyperparameter Tuning
    5. Tracking Metrics During Training
    6. Retraining/Redeployment Evaluation
    7. Unit Testing for Model Training and Serving
    8. Summary
    9. Exam Essentials
    10. Review Questions
  18. Chapter 9: Model Explainability on Vertex AI
    1. Model Explainability on Vertex AI
    2. Summary
    3. Exam Essentials
    4. Review Questions
  19. Chapter 10: Scaling Models in Production
    1. Scaling Prediction Service
    2. Serving (Online, Batch, and Caching)
    3. Google Cloud Serving Options
    4. Hosting Third‐Party Pipelines (MLflow) on Google Cloud
    5. Testing for Target Performance
    6. Configuring Triggers and Pipeline Schedules
    7. Summary
    8. Exam Essentials
    9. Review Questions
  20. Chapter 11: Designing ML Training Pipelines
    1. Orchestration Frameworks
    2. Identification of Components, Parameters, Triggers, and Compute Needs
    3. System Design with Kubeflow/TFX
    4. Hybrid or Multicloud Strategies
    5. Summary
    6. Exam Essentials
    7. Review Questions
  21. Chapter 12: Model Monitoring, Tracking, and Auditing Metadata
    1. Model Monitoring
    2. Model Monitoring on Vertex AI
    3. Logging Strategy
    4. Model and Dataset Lineage
    5. Vertex AI Experiments
    6. Vertex AI Debugging
    7. Summary
    8. Exam Essentials
    9. Review Questions
  22. Chapter 13: Maintaining ML Solutions
    1. MLOps Maturity
    2. Retraining and Versioning Models
    3. Feature Store
    4. Vertex AI Permissions Model
    5. Common Training and Serving Errors
    6. Summary
    7. Exam Essentials
    8. Review Questions
  23. Chapter 14: BigQuery ML
    1. BigQuery – Data Access
    2. BigQuery ML Algorithms
    3. Explainability in BigQuery ML
    4. BigQuery ML vs. Vertex AI Tables
    5. Interoperability with Vertex AI
    6. BigQuery Design Patterns
    7. Summary
    8. Exam Essentials
    9. Review Questions
  24. Appendix: Answers to Review Questions
    1. Chapter 1: Framing ML Problems
    2. Chapter 2: Exploring Data and Building Data Pipelines
    3. Chapter 3: Feature Engineering
    4. Chapter 4: Choosing the Right ML Infrastructure
    5. Chapter 5: Architecting ML Solutions
    6. Chapter 6: Building Secure ML Pipelines
    7. Chapter 7: Model Building
    8. Chapter 8: Model Training and Hyperparameter Tuning
    9. Chapter 9: Model Explainability on Vertex AI
    10. Chapter 10: Scaling Models in Production
    11. Chapter 11: Designing ML Training Pipelines
    12. Chapter 12: Model Monitoring, Tracking, and Auditing Metadata
    13. Chapter 13: Maintaining ML Solutions
    14. Chapter 14: BigQuery ML
  25. Index
  26. End User License Agreement

Product information

  • Title: Official Google Cloud Certified Professional Machine Learning Engineer Study Guide
  • Author(s): Mona Mona, Pratap Ramamurthy
  • Release date: November 2023
  • Publisher(s): Wiley
  • ISBN: 9781119944461