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

ML use cases in manufacturing

Manufacturing is an industry sector that produces tangible finished products. It includes many sub-sectors such as consumer goods, electronics goods, industrial equipment, automobiles, furniture, building materials, sporting goods, clothing, and toys. There are multiple stages in a typical product manufacturing life cycle, including product design, prototyping, manufacturing and assembling, and post-manufacturing service and support. The following diagram shows the typical business functions and flow in the manufacturing sector:

Figure 2.16 – Manufacturing business process flow

AI and ML have played an essential role in the manufacturing process, such as sales forecasting, predictive machine maintenance, quality control and robotic automation for manufacturing quality and yield, and process and supply chain optimization to improve overall operational efficiency.

Engineering and product design

Product design is the...