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

Who this book is for

This book is designed for two primary audiences: developers and cloud architects who are looking for guidance and hands-on learning materials to become ML solutions architects, and experienced ML architecture practitioners and data scientists who are looking to develop a broader understanding of industry ML use cases, enterprise data and ML architecture patterns, data management and ML tools, ML governance, and advanced ML engineering techniques.

This book can also benefit data engineers and cloud system administrators looking to understand how data management and cloud system architecture fit into the overall ML platform architecture.

This book assumes you have some Python programming knowledge and are familiar with AWS services. Some of the chapters are designed for ML beginners to learn the core ML fundamentals, and they might overlap with the knowledge already possessed by experienced ML practitioners.