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

How machines learn

In Chapter 1, Machine Learning and Machine Learning Solutions Architecture, we briefly talked about how ML algorithms can improve themselves by processing data and updating model parameters to generate models (analogous to traditional compiled binary from computer source code). So, how does an algorithm actually learn? In short, ML algorithms learn by optimizing (for example, minimizing or maximizing) an objective function (also known as a loss function). You can think of an objective function as a business metric, such as the difference between the projected sales of a product and the actual sales, and the goal of optimizing this objective would be to reduce the difference between the actual sales number and the projected sales number. To optimize this objective, an ML algorithm would iterate and process through large amounts of historical sales data (training data) and adjust its internal model parameters until it can minimize the differences between the projected...