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Book Overview & Buying
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Table Of Contents
Google Machine Learning and Generative AI for Solutions Architects
By :
Google Machine Learning and Generative AI for Solutions Architects
By:
Overview of this book
Most companies today are incorporating AI/ML into their businesses. Building and running apps utilizing AI/ML effectively is tough. This book, authored by a principal architect with about two decades of industry experience, who has led cross-functional teams to design, plan, implement, and govern enterprise cloud strategies, shows you exactly how to design and run AI/ML workloads successfully using years of experience from some of the world’s leading tech companies.
You’ll get a clear understanding of essential fundamental AI/ML concepts, before moving on to complex topics with the help of examples and hands-on activities. This will help you explore advanced, cutting-edge AI/ML applications that address real-world use cases in today’s market. You’ll recognize the common challenges that companies face when implementing AI/ML workloads, and discover industry-proven best practices to overcome these. The chapters also teach you about the vast AI/ML landscape on Google Cloud and how to implement all the steps needed in a typical AI/ML project. You’ll use services such as BigQuery to prepare data; Vertex AI to train, deploy, monitor, and scale models in production; as well as MLOps to automate the entire process.
By the end of this book, you will be able to unlock the full potential of Google Cloud's AI/ML offerings.
Table of Contents (24 chapters)
Preface
Chapter 1: AI/ML Concepts, Real-World Applications, and Challenges
Chapter 2: Understanding the ML Model Development Life Cycle
Chapter 3: AI/ML Tooling and the Google Cloud AI/ML Landscape
Part 2:Diving in and building AI/ML solutions
Chapter 4: Utilizing Google Cloud’s High-Level AI Services
Chapter 5: Building Custom ML Models on Google Cloud
Chapter 6: Diving Deeper – Preparing and Processing Data for AI/ML Workloads on Google Cloud
Chapter 7: Feature Engineering and Dimensionality Reduction
Chapter 8: Hyperparameters and Optimization
Chapter 9: Neural Networks and Deep Learning
Chapter 10: Deploying, Monitoring, and Scaling in Production
Chapter 11: Machine Learning Engineering and MLOps with Google Cloud
Chapter 12: Bias, Explainability, Fairness, and Lineage
Chapter 13: ML Governance and the Google Cloud Architecture Framework
Chapter 14: Additional AI/ML Tools, Frameworks, and Considerations
Part 3:Generative AI
Chapter 15: Introduction to Generative AI
Chapter 16: Advanced Generative AI Concepts and Use Cases
Chapter 17: Generative AI on Google Cloud
Chapter 18: Bringing It All Together: Building ML Solutions with Google Cloud and Vertex AI
Index