Book Image

The Machine Learning Solutions Architect Handbook

By : David Ping
Book Image

The Machine Learning Solutions Architect Handbook

By: David Ping

Overview of this book

When equipped with a highly scalable machine learning (ML) platform, organizations can quickly scale the delivery of ML products for faster business value realization. There is a huge demand for skilled ML solutions architects in different industries, and this handbook will help you master the design patterns, architectural considerations, and the latest technology insights you’ll need to become one. You’ll start by understanding ML fundamentals and how ML can be applied to solve real-world business problems. Once you've explored a few leading problem-solving ML algorithms, this book will help you tackle data management and get the most out of ML libraries such as TensorFlow and PyTorch. Using open source technology such as Kubernetes/Kubeflow to build a data science environment and ML pipelines will be covered next, before moving on to building an enterprise ML architecture using Amazon Web Services (AWS). You’ll also learn about security and governance considerations, advanced ML engineering techniques, and how to apply bias detection, explainability, and privacy in ML model development. 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 like a true professional.
Table of Contents (17 chapters)
Section 1: Solving Business Challenges with Machine Learning Solution Architecture
Section 2: The Science, Tools, and Infrastructure Platform for Machine Learning
Section 3: Technical Architecture Design and Regulatory Considerations for Enterprise ML Platforms


As artificial intelligence and machine learning (ML) become increasingly prevalent in many industries, there is an increasing demand for ML solutions architects who can translate business needs into ML solutions and design ML technology platforms. This book is designed to help people learn ML concepts, algorithms, system architecture patterns, and ML tools to solve business and technical challenges, with an emphasis on large-scale ML systems architecture and operations in an enterprise setting.

The book first introduces ML and business fundamentals, such as the types of ML, business use cases, and ML algorithms. It then dives deep into data management for ML and the various AWS services for building a data management architecture for ML.

After the data management deep dive, the book focuses on two technical approaches to building ML platforms: using open source technologies such as Kubernetes, Kubeflow, MLflow, and Seldon Core, and the use of managed ML services such as Amazon SageMaker, Step Functions, and CodePipeline.

The book then gets into advanced ML engineering topics, including distributed model training and low-latency model serving to meet large-scale model training and high-performance model serving requirements.

Governance and privacy are important considerations for running models in production. In this book, I also cover ML governance requirements and how an ML platform can support ML governance in areas such as documentation, model inventory, bias detection, model explainability, and model privacy.

Building ML-powered solutions do not always require building ML models or infrastructure from scratch. In the book's last chapter, I will introduce AWS AI services and the problems that AI services can help solve. You will learn the core capabilities of some AI services and where you can use them for building ML-powered business applications.

By the end of this book, you will understand the various business, data science, and technology domains of ML solutions and infrastructure. You will be able to articulate the architecture patterns and considerations for building enterprise ML platforms and develop hands-on skills with various open source and AWS technologies. This book can also help you prepare for ML architecture-related job interviews.