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)
1
Section 1: Solving Business Challenges with Machine Learning Solution Architecture
4
Section 2: The Science, Tools, and Infrastructure Platform for Machine Learning
9
Section 3: Technical Architecture Design and Regulatory Considerations for Enterprise ML Platforms

Chapter 11: ML Governance, Bias, Explainability, and Privacy

So far, you have successfully implemented a machine learning (ML) platform. At this point, you might be thinking that your job is done as an ML Solutions Architect (ML SA) and that the business is ready to deploy models into production. Well, it turns out that there are additional considerations. To put models into production, an organization also needs to put governance control in place to meet both the internal policy and external regulatory requirements. ML governance is usually not the responsibility of an ML SA; however, it is important for an ML SA to be familiar with the regulatory landscape and ML governance framework, especially in regulated industries, such as financial services. So, you should consider these requirements when you evaluate or build an ML solution.

In this chapter, we will provide an overview of the ML governance concept and some key components, such as model registry and model monitoring, in...