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Hands-On Artificial Intelligence on Amazon Web Services : Decrease the Time to Market for AI and ML Applications with the Power of AWS.

Author: Subhashini Tripuraneni; Charles Song
Publisher: Birmingham : Packt Publishing, Limited, 2019.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
References; Chapter 2: 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; Object detector architecture; Component
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Tripuraneni, Subhashini.
Hands-On Artificial Intelligence on Amazon Web Services : Decrease the Time to Market for AI and ML Applications with the Power of AWS.
Birmingham : Packt Publishing, Limited, ©2019
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Subhashini Tripuraneni; Charles Song
ISBN: 9781789531473 1789531470
OCLC Number: 1122923326
Notes: Setting up the project structure
Description: 1 online resource (411 pages)
Contents: Cover; Title Page; Copyright and Credits; About Packt; Contributors; Table of Contents; Preface; Section 1: Introduction and Anatomy of a Modern AI Application; Chapter 1: Introduction to Artificial Intelligence on Amazon Web Services; Technical requirements; What is AI?; Applications of AI; Autonomous vehicles; AI in medical care; Personalized predictive keyboards; Why use Amazon Web Services for AI?; Overview of AWS AI offerings; Hands-on with AWS services; Creating your AWS account; Navigating through the AWS Management Console; Finding AWS services; Choosing the AWS region Test driving the Amazon Rekognition serviceWorking with S3; Identity and Access Management; Getting familiar with the AWS CLI; Installing Python; Installing Python on macOS; Installing Python on Linux; Installing Python on Microsoft Windows; Windows 10; Earlier Windows versions; Installing the AWS CLI; Configuring the AWS CLI; Invoking the Rekognition service using the AWS CLI; Using Python for AI applications; Setting up a Python development environment; Setting up a Python virtual environment with Pipenv; Creating your first Python virtual environment; First project with the AWS SDK Developing a demo application web user interfaceDeploying AI application backends to AWS via Chalice; Deploying a static website via AWS S3; Summary; Further reading; Section 2: Building Applications with AWS AI Services; Chapter 3: Detecting and Translating Text with Amazon Rekognition and Translate; Making the world smaller; Understanding the architecture of Pictorial Translator; Component interactions of Pictorial Translator; Setting up the project structure; Implementing services; Recognition service --
text detection; Translation service --
translating text Storage service --
uploading filesA recommendation on unit testing; Implementing RESTful endpoints; Translate the image text endpoint; Upload the image endpoint; Implementing the web user interface; index.html; scripts.js; Deploying Pictorial Translator to AWS; Discussing project enhancement ideas; Summary; Further reading; Chapter 4: Performing Speech-to-Text and Vice Versa with Amazon Transcribe and Polly; Technical requirements; Technologies from science fiction; Understanding the architecture of Universal Translator; Component interactions of Universal Translator

Abstract:

References; Chapter 2: 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; Object detector architecture; Component interactions of the Object Detector; Creating the base project structure; Developing an AI application locally using AWS Chalice

AI in AWS covers primarily two broad topics - a) how to leverage readily available AI/ML APIs and b) how to build, train and deploy ML models from scratch, to solve diverse business problems, such as demand forecasting, image classification, topic modeling, speech and text recognition. By the end of the book, you will have learned how to build ...

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