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)
1
Section 1: Solving Business Challenges with Machine Learning Solution Architecture
4
Section 2: The Science, Tools, and Infrastructure Platform for Machine Learning
9
Section 3: Technical Architecture Design and Regulatory Considerations for Enterprise ML Platforms

ML use case identification exercise

In this exercise, you are going to apply what you have learned in this chapter to your line of business. The goal is to go through a thinking process to business problems that can potentially be solved with machine learning:

  1. Think about a business operation in your line of business. Create a workflow of the operation and identify any known issues, such as a lack of automation, human errors, and long processing cycles in the workflow.
  2. List the business impact of these issues in terms of lost revenue, increased cost, poor customer and employee satisfaction, and potential regulatory and compliance risk exposure. Try to quantify the business impact as much as possible.
  3. Pick one or two problems with the most significant impact if the problems can be solved. Think about ML approaches (supervised machine learning, unsupervised machine learning, or reinforcement machine learning) to solve the problem.
  4. List the data that could be helpful...