The Machine Learning Solutions Architect Handbook

David Ping

• Éditeur: Packt Publishing
• Année: 2022
• ISBN:
  9781801070416
• Sujet: Computer Science
• Sous-sujet: Entreprise Applications

Build highly secure and scalable machine learning platforms to support the fast-paced adoption of machine learning solutions

Key Features

• Explore different ML tools and frameworks to solve large-scale machine learning challenges in the cloud
• Build an efficient data science environment for data exploration, model building, and model training
• Learn how to implement bias detection, privacy, and explainability in ML model development

Book Description

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.

What you will learn

• Apply ML methodologies to solve business problems
• Design a practical enterprise ML platform architecture
• Implement MLOps for ML workflow automation
• Build an end-to-end data management architecture using AWS
• Train large-scale ML models and optimize model inference latency
• Create a business application using an AI service and a custom ML model
• Use AWS services to detect data and model bias and explain models

Who this book is for

This book is for data scientists, data engineers, cloud architects, and machine learning enthusiasts who want to become machine learning solutions architects. You'll need basic knowledge of the Python programming language, AWS, linear algebra, probability, and networking concepts before you get started with this handbook.

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Section 1: Solving Business Challenges with Machine Learning Solution Architecture

In Section 1, you will learn about some of the core machine learning fundamentals, its challenges, and how machine learning can be applied to real-world business problems, to set the context for the rest of the book.

This section comprises the following chapters:

• Chapter 1, Machine Learning and Machine Learning Solutions Architecture
• Chapter 2, Business Use Cases for Machine Learning

Chapter 1: Machine Learning and Machine Learning Solutions Architecture

The field of artificial intelligence (AI) and machine learning (ML) has had a long history. Over the last 70+ years, ML has evolved from checker game-playing computer programs in the 1950s to advanced AI capable of beating the human world champion in the game of Go. Along the way, the technology infrastructure for ML has also evolved from a single machine/server for small experiments and models to highly complex end-to-end ML platforms capable of training, managing, and deploying tens of thousands of ML models. The hyper-growth in the AI/ML field has resulted in the creation of many new professional roles, such as MLOps engineering, ML product management, and ML software engineering across a range of industries.

Machine learning solutions architecture (ML solutions architecture) is another relatively new discipline that is playing an increasingly critical role in the full end-to-end ML life cycle as ML projects become increasingly complex in terms of business impact, science sophistication, and the technology landscape.

This chapter talks about the basic concepts of ML and where ML solutions architecture fits in the full data science life cycle. You will learn the three main types of ML, including supervised, unsupervised, and reinforcement learning. We will discuss the different steps it will take to get an ML project from the ideas stage to production and the challenges faced by organizations when implementing an ML initiative. Finally, we will finish the chapter by briefly discussing the core focus areas of ML solutions architecture, including system architecture, workflow automation, and security and compliance.

Upon completing this chapter, you should be able to identify the three main ML types and what type of problems they are designed to solve. You will understand the role of an ML solutions architect and what business and technology areas you need to focus on to support end-to-end ML initiatives.

In this chapter, we are going to cover the following main topics:

• What is ML, and how does it work?
• The ML life cycle and its key challenges
• What is ML solutions architecture, and where does it fit in the overall life cycle?

What are AI and ML?

AI can be defined as a machine demonstrating intelligence similar to that of human natural intelligence, such as distinguishing different types of flowers through vision, understanding languages, or driving cars. Having AI capability does not necessarily mean a system has to be powered only by ML. An AI system can also be powered by other techniques, such as rule-based engines. ML is a form of AI that learns how to perform a task using different learning techniques, such as learning from examples using historical data or learning by trial and error. An example of ML would be making credit decisions using an ML algorithm with access to historical credit decision data.

Deep learning (DL) is a subset of ML that uses a large number of artificial neurons (known as an artificial neural network) to learn, which is similar to how a human brain learns. An example of a deep learning-based solution is the Amazon Echo virtual assistant. To better understand how ML works, let's first talk about the different approaches taken by machines to learn. They are as follows:

• Supervised ML
• Unsupervised machine learning
• Reinforcement learning

Let's have a look at each one of them in detail.

Supervised ML

Supervised ML is a type of ML where, when training an ML model, an ML algorithm is provided with the input data features (for example, the size and zip code of houses) and the answers, also known as labels (for example, the prices of the houses). A dataset with labels is called a labeled dataset. You can think of supervised ML as learning by example. To understand what this means, let's use an example of how we humans learn to distinguish different objects. Say you are first provided with a number of pictures of different flowers and their names. You are then told to study the characteristics of the flowers, such as the shape, size, and color for each provided flower name. After you have gone through a number of different pictures for each flower, you are then given flower pictures without the names and asked to distinguish them. Based on what you have learned previously, you should be able to tell the names of flowers if they have the characteristics of the known flowers.

In general, the more training pictures with variations you have looked at during the learning time, the more accurate you will likely be when you try to name flowers in the new pictures. Conceptually, this is how supervised ML works. The following figure (Figure 1.1) shows a labeled dataset being fed into a computer vision algorithm to train an ML model:

Figure 1.1 – Supervised ML

Supervised ML is mainly used for classification tasks that assign a label from a discrete set of categories to an example (for example, telling the names of different objects) and regression tasks that predict a continuous value (for example, estimating the value of something given supporting information). In the real world, the majority of ML solutions are based on supervised ML techniques. The following are some examples of ML solutions that use supervised ML:

• Classifying documents into different document types automatically, as part of a document management workflow. The typical business benefits of ML-based document processing are the reduction of manual effort, which reduces costs, faster processing time, and higher processing quality.
• Assessing the sentiment of news articles to help understand the market perception of a brand or product or facilitate investment decisions.
• Automating the objects or faces detection in images as part of a media image processing workflow. The business benefits this delivers are cost-saving from the reduction of human labor, faster processing, and higher accuracy.
• Predicting the probability that someone will default on a bank loan. The business benefits this delivers are faster decision-making on loan application reviews and approvals, lower processing costs, and a reduced impact on a company's financial statement due to loan defaults.

Unsupervised ML

Unsupervised ML is a type of ML where an ML algorithm is provided with input data features without labels. Let's continue with the flower example, however in this case, you are now only provided with the pictures of the flowers and not their names. In this scenario, you will not be able to figure out the names of the flowers, regardless of how much time you spend looking at the pictures. However, through visual inspection, you should be able to identify the common characteristics (for example, color, size, and shape) of different types of flowers across the pictures, and group flowers with common characteristics in the same group.

This is similar to how unsupervised ML works. Specifically, in this particular case, you have performed the clustering task in unsupervised ML:

Figure 1.2 – Unsupervised ML

In addition to the clustering technique, there are many other techniques in unsupervised ML. Another common and useful unsupervised ML technique is dimensionality reduction, where a smaller number of transformed features represent the original set of features while maintaining the critical information from the original features so that they can be largely reproduced in the number of data dimensions and size. To understand this more intuitively, let's take a look at Figure 1.3:

Figure 1.3 – Reconstruction of an image from reduced features

In this figure, the original picture on the left is transformed to the reduced representation in the middle. While the reduced representati...