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 4: Data Management for Machine Learning

As a practitioner of machine learning (ML) solutions architecture, I often get asked to help provide architecture advice on data management platforms for ML workloads. While data management platform architecture is mainly considered a separate technical discipline, it is an integral part of ML workloads. To design a comprehensive ML platform, ML solutions architects need to be familiar with the key data architecture considerations for machine learning and know the technical design of a data management platform to meet the needs of data scientists and the automated ML pipelines. In this chapter, we will look at where data management intercepts with ML. We will talk about key considerations for designing a data management platform for ML. We will then deep dive into the core architecture components for a data management platform and the relevant AWS technologies and services for building a data management platform on AWS. Finally, you will...