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 2: Business Use Cases for Machine Learning

As a machine learning (ML) practitioner, I often need to develop a deep understanding of different businesses to have effective conversations with the business and technology leaders. This should not come as a surprise since the ultimate goal for any machine learning solution architecture (ML solution architecture) is to solve practical business problems with science and technology solutions. As such, one of the main ML solution architecture focus areas is to develop a broad understanding of different business domains, business workflows, and relevant data. Without this understanding, it would be challenging to make sense of the data and design and develop practical ML solutions for business problems.

In this chapter, you will learn about some real-world ML use cases across several industry verticals. You will develop an understanding of key business workflows and challenges in industries such as financial services and retail,...