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

Introduction to containers

A container is a form of operating system virtualization, and it has been a very popular computing platform for software deployment and running modern software based on micro-services architecture. A container allows you to package and run computer software with isolated dependencies. Compared to server virtualization, such as Amazon EC2 or VMware virtual machines, containers are more lightweight and portable, as they share the same operating system and do not contain operating system images in each container. Each container has its own filesystem, shares of computing resources, and process space for the custom applications running inside it.

While containers may seem like a relatively new transformative technology, the concept of containerization technology was actually born in the 1970s with the chroot system and Unix Version 7. However, container technology did not gain much attraction in the software development community for the next two decades...