Google Machine Learning and Generative AI for Solutions Architects

A Deeper Dive

Spanish version here!

Alright, so I’ve spent some time with “Google Machine Learning and Generative AI for Solutions Architects,” and I’ve gotta say, it’s a pretty impressive piece of work. We all know there are tons of books out there on AI/ML and cloud computing, but this one feels different. It’s not just rehashing the same old theory; it’s like a field manual for building real, production-ready AI systems on Google Cloud.

It’s Like a Real-World Blueprint, Not Just a Textbook

What really sets this book apart is its practical focus. It doesn’t just talk about AI/ML; it shows you how to do it. It walks you through the entire lifecycle, from wrangling messy data to deploying and monitoring models at scale. It’s not just about algorithms and math; it’s about the nuts and bolts of building AI solutions that actually work in the real world. And the hands-on exercises? They’re not just theoretical fluff; they’re actual projects that you can build and learn from.

Google Machine Learning and Generative AI for Solutions Architects + yours truly.

Here’s Where It Really Shines

• MLOps is Front and Center: This isn’t an afterthought. The book dedicates a significant chunk to MLOps, covering everything from automation to monitoring to CI/CD pipelines. It’s clear that the author understands that building a model is only half the battle; the real challenge is keeping it running smoothly in production.
• Generative AI Gets Its Due: It’s not just about traditional ML. The book dives deep into the world of generative AI, covering embeddings, vector databases, RAG, and even LangChain. It’s a great way to get up to speed on the latest trends.
• Real-World Challenges Addressed: It doesn’t shy away from the messy parts of AI/ML. It tackles the common issues we all face, like data quality, bias, model drift, and security. It’s not just about the tech; it’s about the real-world problems we need to solve.
• Google Cloud Expertise: It’s a deep dive into Google Cloud’s AI/ML ecosystem. You get a solid understanding of their services, how they work together, and when to use each one. It’s like having a Google Cloud expert guiding you through the process.
• It’s a Three-in-One Deal: It’s like getting three courses in one: a solid foundation in AI/ML, a comprehensive guide to Google Cloud, and a practical manual for building enterprise-grade solutions.

Table of Contents

Preface

Part 1: The Basics

1. AI/ML Concepts, Real-World Applications, and Challenges

• Terminology — AI, ML, DL, and GenAI
• A brief history of AI/ML
• AI/ML and cloud computing
• ML approaches and use cases
• Supervised learning (SL)
• Unsupervised learning (UL)
• Reinforcement learning (RL)
• A brief discussion of ML basic concepts
• Linear algebra
• Calculus
• Statistics and probability
• Metrics
• Common challenges in developing ML applications
• Gathering, processing, and labeling data
• Organizational challenges
• Operationalization and ongoing management of AI/ML models
• Edge cases
• Summary

2. Understanding the ML Model Development Life Cycle

• An overview of the ML model development life cycle
• SDLC — a quick recap
• Typical ML project stages
• Roles and personas in AI/ML projects
• Common challenges encountered in the ML model development life cycle
• Finding and gathering relevant data
• Picking an algorithm and model architecture
• Data labeling
• Training models
• Configuring and tuning hyperparameters
• Evaluating models
• Deploying models
• Monitoring models after deployment
• Best practices for overcoming common challenges
• Finding and gathering relevant data
• Data labeling
• Picking an algorithm and model architecture
• Training models
• Configuring and tuning hyperparameters
• Deploying models
• Monitoring models after deployment
• Summary

3. AI/ML Tooling and the Google Cloud AI/ML Landscape

• Why Google Cloud?
• Prerequisites for using Google Cloud tools and services
• Security, privacy, and compliance
• Google Cloud services overview
• Google Cloud computing services
• Google Cloud integration services
• Networking and connectivity
• Google Cloud tools for data storage and processing
• Data ingestion
• Data storage
• Data management
• Data processing
• Google Cloud AI tools and AutoML
• NLP
• The Natural Language API
• Computer vision
• 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

4. Utilizing Google Cloud’s High-Level AI Services

• Prerequisites for this chapter
• Cloning this book’s GitHub repository to your local machine
• The Google Cloud console
• Google Cloud project
• Google Cloud Billing
• Google Cloud Shell
• Authentication
• Enabling the relevant Google Cloud APIs
• Storing authentication credentials in environment variables
• Creating a directory and cloning our GitHub repository
• Detecting text in images with the Cloud Vision API
• Using Document AI to extract information from documents
• Document AI concepts
• Performing OCR with Document AI
• Using the Google Cloud Natural Language API to get sentiment analysis insights from textual inputs
• Sentiment analysis with the Natural Language API
• Classifying content with the Natural Language API
• Using Vertex AI AutoML
• Use case — forecasting
• Summary

5. Building Custom ML Models on Google Cloud

• Background information — libraries
• scikit-learn
• Matplotlib
• pandas
• XGBoost
• Prerequisites for this chapter
• Vertex AI Workbench
• Creating a Vertex AI Workbench instance
• Vertex AI Notebook JupyterLab integrations
• Cloning the GitHub repository
• UML with scikit-learn on Vertex AI
• K-means
• Implementing a UML workload in Vertex AI
• Implementing a regression model with scikit-learn on Vertex AI
• Implementing a classification model with XGBoost on Vertex AI
• Decision trees
• Summary

6. Diving Deeper — Preparing and Processing Data for AI/ML Workloads on Google Cloud

• Prerequisites for this chapter
• Enabling APIs
• IAM permissions
• Cloud Storage bucket folders
• Uploading data
• Fundamental concepts in this chapter
• Ingesting data into Google Cloud
• ETL and ELT
• Batch and streaming data processing
• Data pipelines
• Exploring, visualizing, and preparing data
• Batch data pipelines
• Batch data pipeline concepts and tools
• Building our batch data pipeline
• Cloud Composer
• Google Cloud Serverless Spark
• Streaming data pipelines
• Streaming data pipeline concepts and tools
• Building our streaming data pipeline
• Creating a BigQuery dataset
• Creating a BigQuery table
• Creating a Dataflow job from a template
• Verifying data in BigQuery
• Creating a Dataflow notebook
• Verifying data in BigQuery
• Summary

7. Feature Engineering and Dimensionality Reduction

• Fundamental concepts in this chapter
• Dimensions and features
• Overfitting, underfitting, and regularization
• Feature selection and feature engineering
• The curse of dimensionality
• Dimensionality reduction
• t-SNE
• Using PCA and LDA for dimensionality reduction
• Feature engineering
• Vertex AI Feature Store
• Introduction to Vertex AI Feature Store
• Online versus offline feature serving
• Building our feature store
• How features are used during online inference
• Summary

8. Hyperparameters and Optimization

• Prerequisites
• Enabling the Artifact Registry API
• Creating an AI/ML service account
• Concepts
• Model evaluation metrics used in this chapter
• What are hyperparameters?
• Hyperparameter optimization
• Methods for optimizing hyperparameter values
• Hands-on: performing hyperparameter tuning in Vertex AI
• Vertex AI Vizier
• Summary

9. Neural Networks and Deep Learning

• NN and DL concepts
• Neurons and the perceptron
• Backpropagation
• Activation functions
• Libraries
• TensorFlow
• Keras
• Implementing an MLP in TensorFlow
• NN architectures, challenges, and optimization
• Common NN architectures
• Common NN challenges
• Summary

10. Deploying, Monitoring, and Scaling in Production

• How do I make my models available to my applications?
• Fundamental concepts for serving models
• Online and offline model serving
• Vertex AI Model Registry
• Vertex AI prediction service
• A/B testing
• Common challenges of serving models in production
• Deployment infrastructure
• Model availability and scaling in production
• Data quality
• Model/data/concept drift
• Security and privacy
• Model interpretability
• Tracking ML model metadata
• Integration with existing systems
• Monitoring
• Monitoring models in production
• Objective model performance
• Monitoring specifically for data drift
• Anomalous model behavior
• Resource utilization
• Model bias and fairness
• Addressing model performance degradation
• Optimizing for AI/ML at the edge
• Model optimization
• Optimization beyond model techniques
• Summary

11. Machine Learning Engineering and MLOps with Google Cloud

• An introduction to MLOps
• Why MLOps is needed for deploying large-scale ML workloads
• Model management and versioning
• Productivity and automation
• Reproducibility
• Continuous Integration/Continuous Deployment (CI/CD)
• Model validation and testing
• Monitoring and maintenance
• Collaboration and communication
• Regulatory compliance and governance
• MLOps tools
• Pipeline orchestration tools
• Experiment and lineage tracking tools
• Model deployment and monitoring tools
• Model interpretability and explainability tools
• Implementing MLOps on Google Cloud using Vertex AI Pipelines
• Prerequisite: IAM permissions
• Implementation
• Summary

12. Bias, Explainability, Fairness, and Lineage

• An overview of bias, explainability, and fairness in AI/ML
• Bias
• Fairness
• Explainability
• How to detect and mitigate bias in datasets
• Data exploration and visualization
• Specific tools for detecting dependencies between features
• Mechanisms incorporating model prediction results
• Using explainability to understand ML models and reduce bias
• Explainability techniques, methods, and tools
• Feature importance
• Reducing bias and enhancing fairness
• Additional libraries
• The importance of lineage tracking in ML model development
• ML metadata service terminology
• Lineage tracking in Vertex AI
• Summary

13. ML Governance and the Google Cloud Architecture Framework

• ML governance
• Data governance
• ML model governance
• Operationalization of ML governance
• ML governance in different industries and locations
• Keeping up with the evolving landscape of ML governance
• An overview of the Google Cloud Architecture Framework
• Pillar 1 — System design
• Pillar 2 — Operational excellence
• Pillar 3 — Security, privacy, and compliance
• Pillar 4 — Reliability
• Pillar 5 — Cost optimization
• Pillar 6 — Performance optimization
• Architecture Framework concepts about AI/ML workloads on Google Cloud
• Data collection and preparation
• Model building and training
• Model evaluation and deployment
• Summary

14. Additional AI/ML Tools, Frameworks, and Considerations

• Prerequisite topics and steps
• Staging files for serverless Spark MLlib activities
• BQML
• Using BQML
• When to use BQML versus other tools for AI/ML use cases
• BigQuery Studio
• Hardware considerations for AI/ML workloads
• CPUs, GPUs, and TPUs
• Additional open source tools and frameworks –Spark MLlib, Ray, and PyTorch on Google Cloud
• Spark MLlib
• Ray
• PyTorch
• Large-scale distributed model training
• Data parallelism and model parallelism
• Distributed training update process
• Federated learning
• Transitioning to Generative AI
• CNNs and computer vision
• RNNs, LSTMs, and transformers
• Summary

Part 3: Generative AI

15. Introduction to Generative AI

• Fundamentals of GenAI
• What is GenAI?
• What is non-GenAI?
• Diving deeper into GenAI versus non-GenAI
• GenAI techniques and evolution
• Embeddings and latent space
• LLMs
• Evolution of LLMs
• Building LLMs
• Summary

16. Advanced Generative AI Concepts and Use Cases

• Advanced tuning and optimization techniques
• Prompt engineering
• Transfer Learning (TL)
• Aligning with human values and expectations
• Embeddings and vector databases
• Embeddings and similarity of concepts
• Vector databases
• How vector databases work
• Creating embeddings
• Retrieval-Augmented Generation (RAG)
• How RAG works
• Multimodal models
• Why multimodality matters
• Multimodality challenges
• GenAI model evaluation
• Human evaluation
• BLEU
• ROUGE
• IS
• FID
• Auto-raters and side-by-side evaluations
• LangChain
• Summary

17. Generative AI on Google Cloud

• Overview of generative AI in Google Cloud
• Google’s generative AI models
• Open source and third-party generative AI models on Google Cloud
• Vector databases in Google Cloud
• A detailed exploration of Google Cloud generative AI
• Google Cloud Vertex AI Studio
• A detailed exploration of Google Cloud vector database options
• Implementing generative AI solutions on Google Cloud
• Building a Vertex AI Search and Conversation application
• Summary

18. Bringing It All Together: Building ML Solutions with Google Cloud and Vertex AI

• Prerequisites
• Using the Google Cloud Shell
• Building a RAG implementation piece by piece
• Tokens and chunks
• Example RAG solution architecture
• Building the RAG implementation on Google Cloud
• An example business use case and reference architecture
• Additional background on our use case
• The reference architecture
• Building and implementing the use case on Google Cloud
• Recap and next steps
• Next steps

Index

Other Books You May Enjoy

Who Should Grab a Copy?

If you’re a solutions architect, or you’re on that path, this book is a must-have. It’s also a great resource for data scientists who want to move beyond the lab and into production. It’s not a beginner’s book, but it does a good job of explaining complex concepts clearly, so you don’t need to be a math whiz to follow along.

My Final Thoughts:

This book is a fantastic resource for anyone serious about building and deploying AI/ML solutions on Google Cloud. It’s practical, comprehensive, and gives you a real-world perspective on what it takes to succeed. It’s not just about the tech; it’s about the entire process, from start to finish. If you’re looking to level up your AI/ML game and build solutions that actually deliver business value, I’d highly recommend checking it out. It’s not just a book; it’s an investment in your future.

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