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

Chapter 6: Kubernetes Container Orchestration Infrastructure Management

While it is fairly straightforward to build a local data science environment with open source technologies for individual uses in simple machine learning (ML) tasks, it is quite challenging to configure and maintain a data science environment for many users for different ML tasks and track ML experiments. Building an end-to-end ML platform is a complex process, and there are many different architecture patterns and open source technologies available to help. In this chapter, we will cover Kubernetes, an open source container orchestration platform that can serve as the foundational infrastructure for building open source ML platforms. We will discuss the core concept of Kubernetes, its networking architecture and components, and its security and access control. You will also get hands-on with Kubernetes to build a Kubernetes cluster and use it to deploy containerized applications.

Specifically, we will cover...