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 8: Building a Data Science Environment Using AWS ML Services

While some organizations choose to build machine learning (ML) platforms on their own using open source technologies, many other organizations prefer to use fully managed ML services as the foundation for their ML platforms. In this chapter, we will focus on the fully managed ML services offered by AWS. Specifically, you will learn about Amazon SageMaker, a fully managed ML service, and other related services for building a data science environment for data scientists. We will cover specific SageMaker components such as SageMaker Notebook, SageMaker Studio, SageMaker Training Service, and SageMaker Hosting Service. We will also discuss the architecture pattern for building a data science environment, and we will provide a hands-on exercise in building a data science environment.

After completing this chapter, you will be familiar with Amazon SageMaker, AWS CodeCommit, and Amazon ECR and be able to use these services...