Machine Learning in the AWS Cloud

Add Intelligence to Applications with Amazon SageMaker and Amazon Rekognition

Abhishek Mishra

• Publisher: Sybex
• Year: 2019
• Print ISBN: 9781119556718
• eBook ISBN: 9781119556725
• Edition: 1
• Topic: Computer Science
• Subtopic: Computer Science General
• Index: Computer Science

Put the power of AWS Cloud machine learning services to work in your business and commercial applications!

Machine Learning in the AWS Cloud introduces readers to the machine learning (ML) capabilities of the Amazon Web Services ecosystem and provides practical examples to solve real-world regression and classification problems. While readers do not need prior ML experience, they are expected to have some knowledge of Python and a basic knowledge of Amazon Web Services.

Part One introduces readers to fundamental machine learning concepts. You will learn about the types of ML systems, how they are used, and challenges you may face with ML solutions. Part Two focuses on machine learning services provided by Amazon Web Services. You'll be introduced to the basics of cloud computing and AWS offerings in the cloud-based machine learning space. Then you'll learn to use Amazon Machine Learning to solve a simpler class of machine learning problems, and Amazon SageMaker to solve more complex problems.

• Learn techniques that allow you to preprocess data, basic feature engineering, visualizing data, and model building

• Discover common neural network frameworks with Amazon SageMaker

• Solve computer vision problems with Amazon Rekognition

• Benefit from illustrations, source code examples, and sidebars in each chapter

The book appeals to both Python developers and technical/solution architects. Developers will find concrete examples that show them how to perform common ML tasks with Python on AWS. Technical/solution architects will find useful information on the machine learning capabilities of the AWS ecosystem.

Part 1
Fundamentals of Machine Learning

• Chapter 1: Introduction to Machine Learning
• Chapter 2: Data Collection and Preprocessing
• Chapter 3: Data Visualization with Python
• Chapter 4: Creating Machine Learning Models with Scikit-learn
• Chapter 5: Evaluating Machine Learning Models

Chapter 1
Introduction to Machine Learning

WHAT'S IN THIS CHAPTER

• Introduction to the basics of machine learning
• Tools commonly used by data scientists
• Applications of machine learning
• Types of machine learning systems
• Comparison between a traditional and a machine learning system

Hello and welcome to the exciting world of machine learning with Amazon Web Services (AWS). If you have never heard of machine learning until now, you may be tempted to think that it is a recent innovation in computer science that will result in sentient computer programs, significantly more intelligent than humans, that will one day make humans obsolete. There is very little truth in that idea of machine learning. For starters, it is not a recent development. For decades computer scientists have been researching ways to make computers more intelligent, attempting to find ways to teach computers to reason and to make decisions, generalizations, and predictions much like humans do.

Machine learning specifically deals with the problem of creating computer programs that can generalize and predict information reliably, quickly, and with accuracy resembling what a human would do with similar information. Building machine learning models can require a lot of processing power and storage space, and until recently was only possible to implement in very large companies or in academic institutions. Recent advances in storage, processor speeds, GPU technology, and the ability to rapidly create new virtual computing resources in the cloud have finally provided the processing power require to build and deploy machine learning systems at scale, and get results in real time.

Another factor that has contributed to the recent increase in machine learning applications is the availability of excellent tools such as Pandas, Matplotlib, TensorFlow, Scikit-learn, PyTorch, and Jupyter Notebook, which have made it possible for newcomers to start building real-world machine learning applications without having to delve into the complex underlying mathematical concepts.

Cloud computing as we know it today was born in 2006 when Amazon launched its Elastic Compute Cloud (EC2) service. Soon after in 2008, Microsoft launched its Azure service. This was followed by competing offers from other players, including Rackspace, Google, Oracle, and Apple. Building and deploying machine learning applications in the cloud is extremely popular. Most major cloud providers offer services to build and deploy some kind of machine learning applications.

You can find more information on the basics of cloud computing and Amazon Web Services in Chapters 6 and 7. In this chapter you will learn about what machine learning is, how machine learning systems are classified, and examples of real-world applications of machine learning.

What Is Machine Learning?

Machine learning is a discipline within Artificial Intelligence (AI) that deals with creating algorithms that learn from data. Machine learning traces its roots to a computer program created in 1959 by the computer scientist Arthur Samuel while he was working for IBM. Samuel's program could play a game of checkers and was based on assigning each position on the board a score that indicated the likelihood of leading toward winning the game. The positional scores were refined by having the program play against itself, and with each iteration the performance of the program improved. The program was, in effect, learning from experience, and the field of machine learning was born.

A machine learning system can be described as a set of algorithms based on mathematical principles that can mine data to find patterns in the data and then make predictions on new data as it is encountered. Rule-based systems can also make predictions on new data; however, rule-based systems and machine learning systems are not the same. A rule-based system requires a human to find patterns in the data and define a set of rules that can be applied by the algorithm. The rules are typically a series of if-then-else statements that are executed in a specific sequence. A machine learning system, on the other hand, discovers its own patterns and ca...

Table of contents

• Cover
• Table of Contents
• Acknowledgments
• About the Author
• About the Technical Editor
• Introduction
• Part 1: Fundamentals of Machine Learning
• Part 2: Machine Learning with Amazon Web Services
• Index
• End User License Agreement