Architect and run real-world AI/ML solutions at scale on Google Cloud, and discover best practices to address common industry challenges effectively

Key Features

• Understand key concepts, from fundamentals through to complex topics, via a methodical approach
• Build real-world end-to-end MLOps solutions and generative AI applications on Google Cloud
• Get your hands on a code repository with over 20 hands-on projects for all stages of the ML model development lifecycle
• Purchase of the print or Kindle book includes a free PDF eBook

Book Description

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.

What you will learn

• Build solutions with open-source offerings on Google Cloud, such as TensorFlow, PyTorch, and Spark
• Source, understand, and prepare data for ML workloads
• Build, train, and deploy ML models on Google Cloud
• Create an effective MLOps strategy and implement MLOps workloads on Google Cloud
• Discover common challenges in typical AI/ML projects and get solutions from experts
• Explore vector databases and their importance in Generative AI applications
• Uncover new Gen AI patterns such as Retrieval Augmented Generation (RAG), agents, and agentic workflows

Who this book is for

This book is for aspiring solutions architects looking to design and implement AI/ML solutions on Google Cloud. Although this book is suitable for both beginners and experienced practitioners, basic knowledge of Python and ML concepts is required. The book focuses on how AI/ML is used in the real world on Google Cloud. It briefly covers the basics at the beginning to establish a baseline for you, but it does not go into depth on the underlying mathematical concepts that are readily available in academic material.

Table of Contents

1. AI/ML Concepts, Real-World Applications, and Challenges
2. Understanding the ML Model Development Lifecycle
3. AI/ML Tooling and the Google Cloud AI/ML Landscape
4. Utilizing Google Cloud’s High-Level AI Services
5. Building Custom ML Models on Google Cloud
6. Diving Deeper—Preparing and Processing Data for AI/ML Workloads on Google Cloud
7. Feature Engineering and Dimensionality Reduction
8. Hyperparameters and Optimization
9. Neural Networks and Deep Learning
10. Deploying, Monitoring, and Scaling in Production

(N.B. Please use the Read Sample option to see further chapters)

Build highly secure and scalable machine learning platforms to support the fast-paced adoption of machine learning solutions

Key Features

• Explore different ML tools and frameworks to solve large-scale machine learning challenges in the cloud
• Build an efficient data science environment for data exploration, model building, and model training
• Learn how to implement bias detection, privacy, and explainability in ML model development

Book Description

With a highly scalable machine learning (ML) platform, organizations can quickly scale the delivery of ML products for faster business value realization, so there is a huge demand for skilled ML solutions architects in different industries. This hands-on ML book takes you through the design patterns, architectural considerations, and the latest technology that you need to know to become a successful ML solutions architect.

You’ll start by understanding ML fundamentals and how ML can be applied to real-world business problems. Once you’ve explored some of the leading ML algorithms for solving different types of problems, the book will help you get to grips with data management and using ML libraries such as TensorFlow and PyTorch. You’ll learn how to use open source technology such as Kubernetes/Kubeflow to build a data science environment and ML pipelines and then advance to building an enterprise ML architecture using Amazon Web Services (AWS) services. You’ll then cover security and governance considerations, advanced ML engineering techniques, and how to apply bias detection, explainability, and privacy in ML model development. Finally, you’ll get acquainted with AWS AI services and their applications in real-world use cases.

By the end of this book, you’ll be able to design and build an ML platform to support common use cases and architecture patterns.

What you will learn

• Apply ML methodologies to solve business problems
• Design a practical enterprise ML platform architecture
• Implement MLOps for ML workflow automation
• Build an end-to-end data management architecture using AWS
• Train large-scale ML models and optimize model inference latency
• Create a business application using an AI service and a custom ML model
• Use AWS services to detect data and model bias and explain models

Who this book is for

This book is for data scientists, data engineers, cloud architects, and machine learning enthusiasts who want to become machine learning solutions architects. Basic knowledge of the Python programming language, AWS, linear algebra, probability, and networking concepts is assumed.

Table of Contents

1. Machine Learning and Machine Learning Solutions Architecture
2. Business Use Cases for Machine Learning
3. Machine Learning Algorithms
4. Data Management for Machine Learning
5. Open Source Machine Learning Libraries
6. Kubernetes Container Orchestration Infrastructure Management
7. Open Source Machine Learning Platforms
8. Building a Data Science Environment Using AWS ML Services
9. Building an Enterprise ML Architecture with AWS ML Services
10. Advanced ML Engineering
11. ML Governance, Bias, Explainability, and Privacy
12. Building ML Solutions with AWS AI Services