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

Chapter 5: Open Source Machine Learning Libraries

There are multiple technologies available for building machine learning (ML) and data science solutions, in both open source and commercial product spaces. To maintain greater flexibility and customization of their machine learning platforms, some organizations have chosen to invest in in-house data science and engineering teams to build data science platforms using open source technology stacks. Some organizations, however, have adopted commercial products to focus their effort on solving business and data challenges. Some organizations have chosen hybrid architecture to leverage both open source and commercial products for their machine learning platform. As an machine learning solution architecture practitioner, I often need to explain to others what open source machine learning technologies are available and how they can be used for building machine learning solutions.

In the next several chapters, we will focus on various open...