The Machine Learning Solutions Architect Handbook

By David Ping

Early Access

This is an Early Access product. Early Access chapters haven’t received a final polish from our editors yet. Every effort has been made in the preparation of these chapters to ensure the accuracy of the information presented. However, the content in this book will evolve and be updated during the development process.

Learn more
    Advance your knowledge in tech with a Packt subscription

  • Instant online access to over 7,500+ books and videos
  • Constantly updated with 100+ new titles each month
  • Breadth and depth in over 1,000+ technologies

About this book

A highly scalable machine learning platform enables organizations to quickly scale the delivery of ML products for faster business value realization. There is also a huge demand for skillful ML solutions architects in different industries.

This handbook takes you through the design patterns, architectural considerations, and the latest technology that you need to know to become a successful ML solutions architect. You’ll start by understanding core machine learning fundamentals, and how ML can be applied to real-world business problems. Next, you’ll explore some of the leading machine learning and deep learning algorithms for different types of ML problems. The book will further cover data management and architecture considerations for building data science environments using ML libraries such as scikit-learn, Spark, TensorFlow, and PyTorch. You’ll then implement Kubernetes containers for orchestration infrastructure management and later build a data science environment and enterprise ML architecture using AWS ML services. Toward the end, you’ll go through security and compliance considerations, advanced ML engineering techniques, and how to apply ML bias, fairness, and explainability in the end-to-end ML cycle.

By the end of this book, you’ll be able to design and build an ML platform to support ML use cases and architecture patterns.

Publication date:
January 2022


2 Business Use Cases for Machine Learning

As a machine learning (ML) practitioner, I often need to develop a deep understanding of different businesses to have effective conversations with the business and technology leaders. This should not come as a surprise since the ultimate goal for any machine learning solution architecture (ML solution architecture) is to solve practical business problems with science and technology solutions. As such, one of the main ML solution architecture focus areas is to develop a broad understanding of different business domains, business workflows, and relevant data. Without this understanding, it would be challenging to make sense of the data, and design and develop practical ML solutions for business problems.

In this chapter, you will learn about some real-world ML use cases across several industry verticals. You will develop an understanding of key business workflows and challenges in industries such as financial services and retail, and where ML technologies...


ML use cases in financial services

The Financial Services Industry (FSI), one of the most technologically savvy industries, is a front-runner in ML investment and adoption. Over the last several years, I have seen a wide range of ML solutions being adopted across different business functions within financial services. In capital markets, ML is being used in front, middle, and back offices to support investment decisions, trade optimization, risk management, and transaction settlement processing. In insurance, carriers are using ML to streamline underwriting, prevent fraud, and automate claim management. And banks are using ML to improve customer experience, combat fraud, and make loan approval decisions. Next, we will discuss several core business areas within financial services and how ML can be used to solve some of these business challenges.

Capital markets front office

In finance, the front office is the business area that directly generates revenue and mainly consists of customer...


ML use cases in media and entertainment

The media and entertainment (M&E) industry consists of businesses that engage in the production and distribution of films, television, streaming content, music, games, and publishing. The current M&E landscape has been shaped by the increasing adoption of streaming and over-the-top (OTT) content delivery versus traditional broadcasting. M&E customers, faced with ever-increasing media content choices, are shifting their consumption habits and demanding more personalized and enhanced experiences across different devices, anytime, anywhere. M&E companies are also faced with fierce competition in the industry, and to stay competitive, M&E companies need to identify new monetization channels, improve user experience, and improve operational efficiency. The following diagram shows the main steps in the media production and distribution workflow:

Figure 2.10 – Media production and distribution workflow

Over the last several...


ML use cases in healthcare and life sciences

Healthcare and life science is one of the largest and most complex industries. Within this industry, there are several sectors, including the following:

  • Drugs: These are the drug manufacturers such as biotechnology firms, pharmaceutical firms, and the makers of genetics drugs.
  • Medical equipment: These are the companies that manufacture both standard products as well as hi-tech equipment.
  • Managed healthcare: These are the companies that provide health insurance policies.
  • Health facilities: These are the hospitals, clinics, and labs.
  • Government agencies such as CDC and FDA.

The industry has adopted ML for a wide range of use cases such as medical diagnosis and imaging, drug discovery, medical data analysis and management, and disease prediction and treatment.

Medical imaging analysis

Medical imaging is the process and technique of creating a visual representation of the human body for medical analysis. Medical professionals such as radiologists...


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 process where a product designer...


ML use cases in retail

Retail businesses sell consumer products directly to customers through retail stores or e-commerce channels. They get supplies through wholesale distributors or from manufacturers directly. The industry has been going through some significant transformations. While e-commerce is growing much faster than the traditional retail business, traditional brick-and-mortar stores are also transforming in-store shopping experiences to stay competitive. Retailers are looking for new ways to improve the overall shopping experience through both online and physical channels. New trends such as social commerce, augmented reality, virtual assistant shopping, smart stores, and 1:1 personalization are becoming some of the key differentiators among retail businesses.

AI and ML are a key driving force behind the retail industry’s transformation, from inventory optimization and demand forecasting to highly personalized and immersive shopping experiences such as personalized product...


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 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 for building ML solutions...


In this chapter, we covered several ML use cases across multiple industries. You now should have a basic understanding of some top industries and some of the core business workflows in those industries. You have learned about some of the relevant use cases, the business impact that those use cases have, and the ML approaches for solving them.

The next chapter will cover how machines learn and some of the most commonly used ML algorithms.

About the Author

  • David Ping

    David Ping is a Principal ML Architect & Sr. Manager of AI/ML at Amazon Web Services with extensive managerial experience, hands-on technical skills, and domain expertise across multiple industries.

    Browse publications by this author
The Machine Learning Solutions Architect Handbook
Unlock this book and the full library for $5 a month*
Start now