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文前
Foreword
Contributors
About the author
About the reviewer
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Conventions used
Get in touch
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Part 1 The Basics
Chapter 1 AI/ML Concepts, Real-World Applications, and Challenges
Terminology – AI, ML, DL, and GenAI
A brief history of AI/ML
ML approaches and use cases
A brief discussion of ML basic concepts
Common challenges in developing ML applications
Summary
Chapter 2 Understanding the ML Model Development Life Cycle
An overview of the ML model development life cycle
Common challenges encountered in the ML model development life cycle
Best practices for overcoming common challenges
Summary
Chapter 3 AI/ML Tooling and the Google Cloud AI/ML Landscape
Why Google Cloud?
Prerequisites for using Google Cloud tools and services
Google Cloud services overview
Google Cloud tools for data storage and processing
Google Cloud AI tools and AutoML
AutoML
Google Cloud Vertex AI
Standard industry tools on Google Cloud
Choosing the right tool for the job
Summary
Part 2 Diving in and building AI/ML solutions
Chapter 4 Utilizing Google Cloud’s High-Level AI Services
Prerequisites for this chapter
Detecting text in images with the Cloud Vision API
Using Document AI to extract information from documents
Using the Google Cloud Natural Language API to get sentiment analysis insights from textual inputs
Using Vertex AI AutoML
Summary
Chapter 5 Building Custom ML Models on Google Cloud
Background information – libraries
Prerequisites for this chapter
UML with scikit-learn on Vertex AI
Implementing a regression model with scikit-learn on Vertex AI
Implementing a classification model with XGBoost on Vertex AI
Summary
Chapter 6 Diving Deeper – Preparing and Processing Data for AI/ML Workloads on Google Cloud
Prerequisites for this chapter
Fundamental concepts in this chapter
Exploring, visualizing, and preparing data
Batch data pipelines
Building our batch data pipeline
Streaming data pipelines
Building our streaming data pipeline
Summary
Chapter 7 Feature Engineering and Dimensionality Reduction
Fundamental concepts in this chapter
Using PCA and LDA for dimensionality reduction
Feature engineering
Vertex AI Feature Store
Summary
Chapter 8 Hyperparameters and Optimization
Prerequisites
Concepts
What are hyperparameters?
Hyperparameter optimization
Hands-on: performing hyperparameter tuning in Vertex AI
Summary
Chapter 9 Neural Networks and Deep Learning
NN and DL concepts
Libraries
Implementing an MLP in TensorFlow
NN architectures, challenges, and optimization
Summary
Chapter 10 Deploying, Monitoring, and Scaling in Production
How do I make my models available to my applications?
Fundamental concepts for serving models
A/B testing
Common challenges of serving models in production
Monitoring models in production
Optimizing for AI/ML at the edge
Summary
Chapter 11 Machine Learning Engineering and MLOps with Google Cloud
An introduction to MLOps
Why MLOps is needed for deploying large-scale ML workloads
MLOps tools
Implementing MLOps on Google Cloud using Vertex AI Pipelines
Summary
Chapter 12 Bias, Explainability, Fairness, and Lineage
An overview of bias, explainability, and fairness in AI/ML
How to detect and mitigate bias in datasets
Using explainability to understand ML models and reduce bias
The importance of lineage tracking in ML model development
Summary
Chapter 13 ML Governance and the Google Cloud Architecture Framework
ML governance
An overview of the Google Cloud Architecture Framework
Architecture Framework concepts about AI/ML workloads on Google Cloud
Summary
Chapter 14 Additional AI/ML Tools, Frameworks, and Considerations
Prerequisite topics and steps
BQML
Hardware considerations for AI/ML workloads
Additional open source tools and frameworks –Spark MLlib, Ray, and PyTorch on Google Cloud
Large-scale distributed model training
Transitioning to Generative AI
Summary
Part 3 Generative AI
Chapter 15 Introduction to Generative AI
Fundamentals of GenAI
GenAI techniques and evolution
LLMs
Summary
Chapter 16 Advanced Generative AI Concepts and Use Cases
Advanced tuning and optimization techniques
Embeddings and vector databases
RAG
Multimodal models
GenAI model evaluation
LangChain
Summary
Chapter 17 Generative AI on Google Cloud
Overview of generative AI in Google Cloud
A detailed exploration of Google Cloud generative AI
Implementing generative AI solutions on Google Cloud
Summary
Chapter 18 Bringing It All Together: Building ML Solutions with Google Cloud and Vertex AI
Prerequisites
Building a RAG implementation piece by piece
An example business use case and reference architecture
Building and implementing the use case on Google Cloud
Recap and next steps
Index
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Google Machine Learning and Generative AI for Solutions Architects