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

Understanding ML bias and explainability

One of the key focus areas for ML governance is bias detection and model explainability. Having ML models exhibiting biased behaviors not only subjects an organization to potential legal consequences but could also result in a public relations nightmare. Specific laws and regulations, such as Equal Credit Opportunity Act, prohibit discrimination in business transactions, such as credit transactions based on race, color, religion, sex, nationality origin, marital status, and age. Some other examples of laws against discrimination include the Civil Rights Act of 1964 and Age Discrimination in Employment Act of 1967.

ML bias can result from the underlying prejudice in data. Since ML models are trained using data, if the data contains bias, then the trained model will also exhibit biased behaviors. For example, if you build an ML model to predict a loan default rate as part of the loan application review process, and you use race as one of the...