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

Key requirements for an enterprise ML platform

To deliver the business values for ML at scale, organizations need to be able to experiment quickly with different scientific approaches, ML technologies, and datasets at scale. Once the ML models have been trained and validated, they need to be deployed to production with minimal friction. While there are similarities between a traditional enterprise software system and an ML platform, such as scalability and security, an enterprise ML platform poses many unique challenges, such as integrating with the data platform and high-performance computing infrastructure for large-scale model training. Now, let's talk about some specific enterprise ML platform requirements:

  • Support for the end-to-end ML life cycle: An enterprise ML platform needs to support both data science experimentation and production-grade operations/deployments. In Chapter 8, Building a Data Science Environment Using AWS ML Services, we learned about the key...