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

Enterprise ML architecture pattern overview

Building an enterprise ML platform on AWS starts with creating different environments to enable different data science and operations functions. The following diagram shows the core environments that normally make up an enterprise ML platform. From an isolation perspective, in the context of the AWS cloud, each environment in the following diagram is a separate AWS account:

Figure 9.1 – Enterprise ML architecture environments

As we discussed in Chapter 8, Building a Data Science Environment Using AWS ML Services, data scientists use the data science environment for experimentation, model building, and tuning. Once these experiments are completed, the data scientists commit their work to the proper code and data repositories. The next step is to train and tune the ML models in a controlled and automated environment using the algorithms, data, and training scripts that were created by the data scientists. This...