Book Image

Hands-On Artificial Intelligence on Amazon Web Services

By : Subhashini Tripuraneni, Charles Song
1 (1)
Book Image

Hands-On Artificial Intelligence on Amazon Web Services

1 (1)
By: Subhashini Tripuraneni, Charles Song

Overview of this book

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.

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Table of Contents (19 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Section 1: Introduction and Anatomy of a Modern AI Application
Section 1: Introduction and Anatomy of a Modern AI Application
2
Introduction to Artificial Intelligence on Amazon Web Services
Introduction to Artificial Intelligence on Amazon Web Services
Technical requirements
What is AI?
Overview of AWS AI offerings
Getting familiar with the AWS CLI
Using Python for AI applications
First project with the AWS SDK
Summary
References
3
Anatomy of a Modern AI Application
Anatomy of a Modern AI Application
Technical requirements
Understanding the success factors of artificial intelligence applications
Understanding the architecture design principles for AI applications
Understanding the architecture of modern AI applications
Creation of custom AI capabilities
Working with a hands-on AI application architecture
Developing an AI application locally using AWS Chalice
Developing a demo application web user interface
Summary
Further reading
4
Section 2: Building Applications with AWS AI Services
Section 2: Building Applications with AWS AI Services
5
Detecting and Translating Text with Amazon Rekognition and Translate
Detecting and Translating Text with Amazon Rekognition and Translate
Making the world smaller
Understanding the architecture of Pictorial Translator
Setting up the project structure
Implementing services
Implementing RESTful endpoints
Implementing the web user interface
Deploying Pictorial Translator to AWS
Discussing project enhancement ideas
Summary
Further reading
6
Performing Speech-to-Text and Vice Versa with Amazon Transcribe and Polly
Performing Speech-to-Text and Vice Versa with Amazon Transcribe and Polly
Technical requirements
Technologies from science fiction
Understanding the architecture of Universal Translator
Setting up the project structure
Implementing services
Implementing RESTful endpoints
Implementing the Web User Interface
Deploying the Universal Translator to AWS
Discussing the project enhancement ideas
Summary
References
7
Extracting Information from Text with Amazon Comprehend
Extracting Information from Text with Amazon Comprehend
Technical requirements
Working with your Artificial Intelligence coworker
Understanding the Contact Organizer architecture
Setting up the project structure
Implementing services
Implementing RESTful endpoints
Implementing the web user interface
Deploying the Contact Organizer to AWS
Discussing the project enhancement ideas
Summary
Further reading
8
Building a Voice Chatbot with Amazon Lex
Building a Voice Chatbot with Amazon Lex
Understanding the friendly human-computer interface
Contact assistant architecture
Understanding the Amazon Lex development paradigm
Setting up the contact assistant bot
Integrating the contact assistant into applications
Summary
Further reading
9
Section 3: Training Machine Learning Models with Amazon SageMaker
Section 3: Training Machine Learning Models with Amazon SageMaker
10
Working with Amazon SageMaker
Working with Amazon SageMaker
Technical requirements
Preprocessing big data through Spark EMR
Conducting training in Amazon SageMaker
Deploying the trained Object2Vec and running inference
Running hyperparameter optimization (HPO)
Understanding the SageMaker experimentation service
Bring your own model – SageMaker, MXNet, and Gluon
Bring your own container – R model
Summary
Further reading
11
Creating Machine Learning Inference Pipelines
Creating Machine Learning Inference Pipelines
Technical requirements
Understanding the architecture of the inference pipeline in SageMaker
Creating features using Amazon Glue and SparkML
Identifying topics by training NTM in SageMaker
Running online versus batch inferences in SageMaker
Summary
Further reading
12
Discovering Topics in Text Collection
Discovering Topics in Text Collection
Technical requirements
Reviewing topic modeling techniques
Understanding how the Neural Topic Model works
Training NTM in SageMaker
Deploying the trained NTM model and running the inference
Summary
Further reading
13
Classifying Images Using Amazon SageMaker
Classifying Images Using Amazon SageMaker
Technical requirements
Walking through convolutional neural and residual networks
Classifying images through transfer learning in Amazon SageMaker
Performing inference through Batch Transform
Summary
Further reading
14
Sales Forecasting with Deep Learning and Auto Regression
Sales Forecasting with Deep Learning and Auto Regression
Technical requirements
Understanding traditional time series forecasting
How the DeepAR model works
Understanding model sales through DeepAR
Predicting and evaluating sales
Summary
Further reading
15
Section 4: Machine Learning Model Monitoring and Governance
Section 4: Machine Learning Model Monitoring and Governance
16
Model Accuracy Degradation and Feedback Loops
Model Accuracy Degradation and Feedback Loops
Technical requirements
Monitoring models for degraded performance
Developing a use case for evolving training data – ad-click conversion
Creating a machine learning feedback loop
Summary
Further reading
17
What Is Next?
What Is Next?
Summarizing the concepts we learned in Part I
Summarizing the concepts we learned in Part II
Summarizing the concepts we learned in Part III
Summarizing the concepts we learned in Part IV
What's next?
Summary
18
Other Books You May Enjoy
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Deploying the trained NTM model and running the inference

In this section, we will deploy the NTM model, run the inference, and interpret the results. Let's get started:

  1. First, we deploy the trained NTM model as an endpoint, as follows:
ntm_predctr = ntm_estmtr.deploy(initial_instance_count=1, instance_type='ml.m4.xlarge')

In the preceding code, we call the deploy() method of the SageMaker Estimator object, ntm_estmtr, to create an endpoint. We pass the number and type of instances required to deploy the model. The NTM Docker image is used to create the endpoint. SageMaker takes a few minutes to deploy the model. The following screenshot shows the endpoint that was provisioned:

You can see the endpoint you've created by navigating to the SageMaker service, going to the left navigation pane, looking under the Inference section, and clicking on Endpoints.

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