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

Testing your knowledge

Alright! You have just completed this chapter. Let's see if you have understood and retained the knowledge you have just acquired.

Take a look at the list of the following scenarios and determine which of the three ML types can be applied (supervised, unsupervised, or reinforcement):

  1. There is a list of online feedback on products. Each comment has been labeled with a sentiment class (for example, positive, negative, or neutral). You have been asked to build an ML model to predict the sentiment of new feedback.
  2. You have historical house pricing information and details about the house, such as zip code, number of bedrooms, house size, and house condition. You have been asked to build an ML model to predict the price of a house.
  3. You have been asked to identify potentially fraudulent transactions on your company's e-commerce site. You have data such as historical transaction data, user information, credit history, devices, and network access data. However, you don't know which transactions are fraudulent.

Take a look at the following questions on the ML life cycle and ML solutions architecture to see how you would answer them:

  1. There is a business workflow that processes a request with a set of well-defined decision rules, and there is no tolerance to deviate from the decision rules when making decisions. Should you consider ML to automate the business workflow?
  2. You have deployed an ML model into production. However, you do not see the expected improvement in the business KPIs. What should you do?
  3. There is a manual process that's currently handled by a small number of people. You found an ML solution that can automate this process, however, the cost of building and running the ML solution is higher than the cost saved from automation. Should you proceed with the ML project?
  4. As an ML solutions architect, you have been asked to validate an ML approach for solving a business problem. What steps would you take to validate the approach?