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

What is ML governance and why is it needed?

ML governance is a set of policies, processes, and activities by which an organization manages, controls, and monitors an ML model's life cycle, dependencies, access, and performance to avoid or minimize financial risk, reputation risk, compliance risk, and legal risk.

The stakes in model risk management are high. To put this into context, let's revisit the impact of the financial crisis in 2007 and 2008 due to inadequate ML governance. Many of us probably still vividly remember the aftermath of the great recession caused by the crisis, where millions of people were impacted in terms of their jobs, investments, or both, and many of the largest financial institutions were brought to their knees and went out of business. The government had to step in to bail out many institutions such as Fannie Mae and Freddie Mac. This crisis was caused in large part by the flawed model risk management process and governance across financial...