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

Chapter 9: Building an Enterprise ML Architecture with AWS ML Services

To support a large number of fast-moving machine learning (ML) initiatives, many organizations often decide to build enterprise ML platforms capable of supporting the full ML life cycle, as well as a wide range of usage patterns, which also needs to be automated and scalable. As a practitioner, I have often been asked to provide architecture guidance on how to build enterprise ML platforms. In this chapter, we will discuss the core requirements for enterprise ML platform design and implementation. We will cover topics such as workflow automation, infrastructure scalability, and system monitoring. You will learn about architecture patterns for building technology solutions that automate the end-to-end ML workflow and deployment at scale. We will also dive deep into other core enterprise ML architecture components such as model training, model hosting, the feature store, and the model registry at enterprise scale...