In this chapter, we discussed the importance of good architecture design for artificial intelligence applications. We created a reference architecture design for web applications that will be the template for all of our hands-on projects in part 2 of this book. Using this reference architecture, we recreated the Amazon Rekognition demo application using several AWS tools and services in the serverless style. We built the demo application's backend with AWS Chalice and boto3 and leveraged AWS S3 and Amazon Rekognition to provide the business capabilities. Through the hands-on project, we showed you how architecture boundaries and good design allow for flexible application development and evolution. We also built a simple web user interface for the demo application with HTML, CSS, and JavaScript. Finally, we deployed the demo application as a serverless application...
Hands-On Artificial Intelligence on Amazon Web Services
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Hands-On Artificial Intelligence on Amazon Web Services
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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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Current Title:
Hands-On Artificial Intelligence on Amazon Web Services
Table of Contents (19 chapters)
Preface
Free Chapter
Section 1: Introduction and Anatomy of a Modern AI Application
Introduction to Artificial Intelligence on Amazon Web Services
Anatomy of a Modern AI Application
Section 2: Building Applications with AWS AI Services
Detecting and Translating Text with Amazon Rekognition and Translate
Performing Speech-to-Text and Vice Versa with Amazon Transcribe and Polly
Extracting Information from Text with Amazon Comprehend
Building a Voice Chatbot with Amazon Lex
Section 3: Training Machine Learning Models with Amazon SageMaker
Working with Amazon SageMaker
Creating Machine Learning Inference Pipelines
Discovering Topics in Text Collection
Classifying Images Using Amazon SageMaker
Sales Forecasting with Deep Learning and Auto Regression
Section 4: Machine Learning Model Monitoring and Governance
Model Accuracy Degradation and Feedback Loops
What Is Next?
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