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 1: Machine Learning and Machine Learning Solutions Architecture

The field of artificial intelligence (AI) and machine learning (ML) has had a long history. Over the last 70+ years, ML has evolved from checker game-playing computer programs in the 1950s to advanced AI capable of beating the human world champion in the game of Go. Along the way, the technology infrastructure for ML has also evolved from a single machine/server for small experiments and models to highly complex end-to-end ML platforms capable of training, managing, and deploying tens of thousands of ML models. The hyper-growth in the AI/ML field has resulted in the creation of many new professional roles, such as MLOps engineering, ML product management, and ML software engineering across a range of industries.

Machine learning solutions architecture (ML solutions architecture) is another relatively new discipline that is playing an increasingly critical role in the full end-to-end ML life cycle as ML projects become increasingly complex in terms of business impact, science sophistication, and the technology landscape.

This chapter talks about the basic concepts of ML and where ML solutions architecture fits in the full data science life cycle. You will learn the three main types of ML, including supervised, unsupervised, and reinforcement learning. We will discuss the different steps it will take to get an ML project from the ideas stage to production and the challenges faced by organizations when implementing an ML initiative. Finally, we will finish the chapter by briefly discussing the core focus areas of ML solutions architecture, including system architecture, workflow automation, and security and compliance.

Upon completing this chapter, you should be able to identify the three main ML types and what type of problems they are designed to solve. You will understand the role of an ML solutions architect and what business and technology areas you need to focus on to support end-to-end ML initiatives.

In this chapter, we are going to cover the following main topics:

  • What is ML, and how does it work?
  • The ML life cycle and its key challenges
  • What is ML solutions architecture, and where does it fit in the overall life cycle?