What this book covers
Chapter 1, AI/ML Concepts, Real-World Applications, and Challenges, sets the stage for the AI/ML topics that will be used in more depth in the rest of the book.
Chapter 2, Understanding the ML Model Development Life Cycle, introduces the common steps that are found in typical, well-structured ML projects.
Chapter 3, AI/ML Tooling and the Google Cloud AI/ML Landscape, describes the tools for building AI/ML solutions, specifically on Google Cloud
Chapter 4, Utilizing Google Cloud’s High-Level AI Services, starts to use the tools to implement real-world AI/ML use cases.
Chapter 5, Building Custom ML Models on Google Cloud, gets hands-on with Scikit-learn on Google Cloud.
Chapter 6, Diving Deeper - Preparing and Processing Data for AI/ML Workloads on Google Cloud, outlines the procedures to implement a complex data processing workload.
Chapter 7, Feature Engineering and Dimensionality Reduction, builds on the data processing themes from the previous chapter, this chapter focuses on one of the most important steps in an ML project: feature engineering.
Chapter 8, Hyperparameters and Optimization, outlines the importance of hyperparameter tuning, and how to implement this in Vertex AI.
Chapter 9, Neural Networks and Deep Learning, provides an overview of the use of neural networks to solve more complex problems in AI/ML.
Chapter 10, Deploying, Monitoring, and Scaling in Production, focuses on how to productionize ML models, the kinds of challenges that are faced at this point in the process, and how to use Vertex AI to address some of those challenges.
Chapter 11, Machine Learning Engineering and MLOps with Google Cloud, dives into more detail on deployment concepts and challenges and describes the importance of MLOps in addressing these challenges for large-scale production AI/ML workloads.
Chapter 12, Bias, Explainability, Fairness, and Lineage, discusses these concepts in detail, and explains how to effectively incorporate these concepts into the readers’ ML workloads.
Chapter 13, ML Governance and the Google Cloud Architecture Framework, describes architectural design patterns for AI/ML workloads on Google Cloud.
Chapter 14, Additional AI/ML Tools, Frameworks, and Considerations, branches out into additional popular AI/ML frameworks such as PyTorch, Spark ML, and BigQuery ML.
Chapter 15, Introduction to Generative AI, outlines the fundamental concepts of Generative AI and focus on its distinctions from “traditional” predictive AI /ML.
Chapter 16, Advanced Generative AI Concepts and Use Cases, dives deeper into embeddings, vector databases, and frameworks such as RAG and LangChain.
Chapter 17, Generative AI on Google Cloud, explores Google Cloud’s Generative AI products and solutions.
Chapter 18, Bringing It All Together: Building ML Solutions with Google Cloud and Vertex AI, brings together all of the major elements we’ve learned throughout the book and helps us in building reference architectures.
