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

Understanding the ML governance framework

ML governance is complex as it deals with complex internal and regulatory policies. There are many stakeholders and technology systems involved in the full ML life cycle. Furthermore, the opaque nature of many ML models, data dependencies, ML privacy, and the stochastic behaviors of many ML algorithms make ML governance more challenging.

The governance body in an organization is responsible for establishing policies and the ML governance framework. To operationalize ML risk management, many organizations set up three lines of defense for their organizational structure:

  • The first line of defense is owned by the business operations. This line of defense focuses on the development and use of ML models. The business operations are responsible for creating and retaining all data and model assumptions, model behavior, and model performance metrics in structured documents based on model classification and risk exposure. Models are tested...