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

Designing an ML platform for governance

ML technology systems are critical in the overall operations of ML governance processes and activities. First, these technology systems need to be designed and built to meet the internal and external policies and guidelines themselves. Second, technology can help with simplifying and automating ML governance activities. The following diagram shows the various ML governance touchpoints in an enterprise ML platform:

Figure 11.1 – ML platform and ML governance

When an ML platform is built with ML governance in mind, it can capture and supply information to help with the three lines of defense and let you streamline the model risk management workflows. The types of tools that are used for ML governance include online data stores, workflow applications, document sharing systems, and model inventory databases. Now, let's take a closer look at some of the core ML governance components and where an ML platform...