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

Hands-on exercise – data management for ML

In this hands-on exercise, you will build a data management platform for a fictitious retail bank to support an ML workflow. We will build the data management platform on AWS using various AWS technologies. If you don't have an AWS account, you can create one by following the instructions at

The data management platform we create will have the following key components:

  • A data lake environment for data management
  • A data ingestion component for ingesting files to the data lake
  • A data discovery and query component
  • A data processing component

The following diagram shows the data management architecture we will build in this exercise:

Figure 4.6 – Data management architecture for the hands-on exercise

Let's get started with building out this architecture on AWS.

Creating a data lake using Lake Formation

We will build the data lake...