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Hands-On Artificial Intelligence on Amazon Web Services
Hands-On Artificial Intelligence on Amazon Web Services

Hands-On Artificial Intelligence on Amazon Web Services: Decrease the time to market for AI and ML applications with the power of AWS

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Hands-On Artificial Intelligence on Amazon Web Services

Introduction to Artificial Intelligence on Amazon Web Services

In this chapter, we will start with a high-level overview of artificial intelligence (AI), including its history and the broad set of methods that it uses. Then, we will take a look at a few applications of AI that have the potential to profoundly change our world. With growing interests in AI, many companies, including Amazon, are offering a plethora of tools and services to help developers create intelligent-enabled applications. We will provide a high-level overview of AI offerings from Amazon Web Services, and we will also provide our guidance on how to best leverage them. Being a hands-on book, we will quickly dive into intelligent-enabled application development with Amazon Web Services.

We will cover the following topics:

  • Overview of AI and its applications.
  • Understanding the different types of Amazon Web Services...

Technical requirements

What is AI?

AI is an umbrella term that describes a branch of computer science that aims to create intelligent agents. The field of AI is highly technical and specialized; there is a broad set of theories, methods, and technologies in AI that allow computers to see (computer vision), to hear (speech recognition), to understand (natural language processing), to speak (text-to-speech), and to think (knowledge reasoning and planning).

It may seem that AI is a buzzword of our current times, but it has existed since the 1950s, when early work on artificial neural networks that mimic led the human brain stirred up excitement for thinking machines. With all the fanfare it receives in the media today, it is hard to believe that this field had to endure two AI winters, where interest in AI research and development dwindled. Today, AI has become popular again, thanks to the increased volume...

Overview of AWS AI offerings

To better understand AWS AI offerings, we can group the services into two main groups.

The following diagram shows the subsets of AWS AI capabilities and AWS ML platforms that we will be covering in this book, organized by the two groups:

The list of AWS ML services is growing every year. For example, Amazon Personalize, Forecast, Textract, and DeepRacer were announced at the AWS re:Invent 2018 conference, and were in limited preview. These services became available for general use around mid 2019.

The first group in the preceding diagram are the AWS AI capabilities. These services are built on top of AWS pre-trained AI technologies. They work right out of the box to provide ready-made intelligence for your applications. You do not need to understand the AI techniques that make them tick, and you do not need to maintain the infrastructure to host...

Getting familiar with the AWS CLI

The AWS CLI is a tool that allows you to interact with AWS services by issuing commands from your Terminal shell. Earlier in this chapter, we interacted with a few AWS services through the web-based AWS Management Console. While the web console is the easiest interface for new users to get familiar with AWS, it can be cumbersome to use during the software development process. With the AWS CLI tool, you get all of the same functionalities as the web console but at your fingertips in the Terminal shell, where most of your development tools reside. This way, your development process is more fluid without the context switching to and from the browser.

The AWS CLI is primarily distributed via the Python package manager; therefore, you need to install Python on your development machine first. Note that we will be using Python for the development of...

Using Python for AI applications

Python is one of the most popular programming languages. Thanks to its popularity with the data science and ML community, Python is also one of the fastest-growing programming languages. There is a large number of add-on libraries from its developers and the open source community. These libraries enable Python developers to do almost anything, from data analytics to deep neural networks, from simple scripting to web application development.

For AI and ML, Python is the de facto language. There's the popular scikit-learn library that gives developers access to many useful ML algorithms. There are also many libraries for deep neural networks, such as MXNet and TensorFlow.

We will be using Python for every hands-on projects throughout this book:

  • In the first half of this book, we will create intelligent-enabled solutions using AWS AI services...

First project with the AWS SDK

Now, let's write our first Python application that will detect the objects in the images that are stored in an S3 bucket. To do this, we will leverage boto3 to interact with both the Amazon S3 service and the Amazon Rekognition service:

You can use any text editor, or your favorite Python Integrated Development Environment (IDE), to create the Python source files. If you don't have a preference, we recommend that you check out JetBrains PyCharm, https://www.jetbrains.com/pycharm/, a cross-platform Python IDE that provides code editing, code analysis, a graphical debugger, an integrated unit tester, and integration with a version control system.
  1. The first source file that we will create is storage_service.py. Create this source file in the ObjectDetectionDemo directory. The following is the Python code for storage_service.py:
import boto3...

Summary

In this chapter, we learned that AI has existed for a long time, but it is becoming popular again due to the renewed interested in ML, more specifically, with artificial neural networks. We looked at a few real-world applications of AI and ML. We got an overview of the two groups of AI offerings from AWS; the AWS AI services that give you a quick lift when developing intelligent-enabled applications, and the AWS ML platforms that allow you to build custom AI capabilities that are tailored to your application. We recommended that you try to leverage the provided AWS AI services first, and only develop custom AI capabilities if you have a specialized need and you have a data-competitive advantage.

We got familiar with several services on AWS through hands-on experience, including the AI services and the other complimentary cloud services. We also set up our local development...

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Key benefits

  • Explore popular machine learning and deep learning services with their underlying algorithms
  • Discover readily available artificial intelligence(AI) APIs on AWS like Vision and Language Services
  • Design robust architectures to enable experimentation, extensibility, and maintainability of AI apps

Description

From data wrangling through to translating text, you can accomplish this and more with the artificial intelligence and machine learning services available on AWS. With this book, you’ll work through hands-on exercises and learn to use these services to solve real-world problems. You’ll even design, develop, monitor, and maintain machine and deep learning models on AWS. The book starts with an introduction to AI and its applications in different industries, along with an overview of AWS artificial intelligence and machine learning services. You’ll then get to grips with detecting and translating text with Amazon Rekognition and Amazon Translate. The book will assist you in performing speech-to-text with Amazon Transcribe and Amazon Polly. Later, you’ll discover the use of Amazon Comprehend for extracting information from text, and Amazon Lex for building voice chatbots. You will also understand the key capabilities of Amazon SageMaker such as wrangling big data, discovering topics in text collections, and classifying images. Finally, you’ll cover sales forecasting with deep learning and autoregression, before exploring the importance of a feedback loop in machine learning. By the end of this book, you will have the skills you need to implement AI in AWS through hands-on exercises that cover all aspects of the ML model life cycle.

Who is this book for?

This book is for data scientists, machine learning developers, deep learning researchers, and artificial intelligence enthusiasts who want to harness the power of AWS to implement powerful artificial intelligence solutions. A basic understanding of machine learning concepts is expected.

What you will learn

  • Gain useful insights into different machine and deep learning models
  • Build and deploy robust deep learning systems to production
  • Train machine and deep learning models with diverse infrastructure specifications
  • Scale AI apps without dealing with the complexity of managing the underlying infrastructure
  • Monitor and Manage AI experiments efficiently
  • Create AI apps using AWS pre-trained AI services
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Table of Contents

18 Chapters
Section 1: Introduction and Anatomy of a Modern AI Application Chevron down icon Chevron up icon
Introduction to Artificial Intelligence on Amazon Web Services Chevron down icon Chevron up icon
Anatomy of a Modern AI Application Chevron down icon Chevron up icon
Section 2: Building Applications with AWS AI Services Chevron down icon Chevron up icon
Detecting and Translating Text with Amazon Rekognition and Translate Chevron down icon Chevron up icon
Performing Speech-to-Text and Vice Versa with Amazon Transcribe and Polly Chevron down icon Chevron up icon
Extracting Information from Text with Amazon Comprehend Chevron down icon Chevron up icon
Building a Voice Chatbot with Amazon Lex Chevron down icon Chevron up icon
Section 3: Training Machine Learning Models with Amazon SageMaker Chevron down icon Chevron up icon
Working with Amazon SageMaker Chevron down icon Chevron up icon
Creating Machine Learning Inference Pipelines Chevron down icon Chevron up icon
Discovering Topics in Text Collection Chevron down icon Chevron up icon
Classifying Images Using Amazon SageMaker Chevron down icon Chevron up icon
Sales Forecasting with Deep Learning and Auto Regression Chevron down icon Chevron up icon
Section 4: Machine Learning Model Monitoring and Governance Chevron down icon Chevron up icon
Model Accuracy Degradation and Feedback Loops Chevron down icon Chevron up icon
What Is Next? Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Top Reviews
Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.2
(6 Ratings)
5 star 66.7%
4 star 16.7%
3 star 0%
2 star 0%
1 star 16.7%
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Amazon Customer Dec 30, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This is a fantastic book which has helped me a lot in my studies of machine learning on AWS. The book is detailed, easy to understand, and provides clear and comprehensive instruction on designing machine learning applications with AWS. I highly recommend this book to any data scientists, researchers, or developers looking to further their understanding in the area.
Amazon Verified review Amazon
Mesfin D. Dec 26, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This is a great book covering different topics in Machine Learning and showing how one can use Amazon Web Services infrastructure and tools to deploy an end-to-end application. Starting from creating AWS account creation, it guides readers to implement and launch their ML application covering different topics such as object detection, text and speech processing. I highly recommend it.
Amazon Verified review Amazon
Jayaprakash Bandu Dec 29, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Has very well written content for beginners starting using AWS for AI. It shows usage of Chalice to create and deploy application. It then has setting up and using the Amazon services Rekognition, Lex and Sage Maker. In lex it shows about creating and using Intents to develop a Chatbot. In sgaemaker it first goes about training with object2vec algorithm.Gives step by step guide to make your own models for texts as well as images. It then introduces using DeepAr service and XGboost algorithm which keeps the reader to try out more advanced concepts with the services.
Amazon Verified review Amazon
Amazon Customer Dec 30, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This is a very well organized book for anyone wanting to go from 0 to 100 in quick time on aws. While there is a ton of content on aws this book is especially appealing and useful given its structure and practical examples and try outs..highly recommended for folks aspiring to work on aws.
Amazon Verified review Amazon
kevin sunil Nov 21, 2019
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
It's a great book with a lot of knowledge on different machine and deep learning models. It's also into creation of AI apps using AWS. This is a good buy!
Amazon Verified review Amazon
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