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Hands-On Artificial Intelligence on Amazon Web Services

You're reading from   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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Product type Paperback
Published in Oct 2019
Publisher Packt
ISBN-13 9781789534146
Length 426 pages
Edition 1st Edition
Tools
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Authors (2):
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Subhashini Tripuraneni Subhashini Tripuraneni
Author Profile Icon Subhashini Tripuraneni
Subhashini Tripuraneni
Charles Song Charles Song
Author Profile Icon Charles Song
Charles Song
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Toc

Table of Contents (19) Chapters Close

Preface 1. Section 1: Introduction and Anatomy of a Modern AI Application FREE CHAPTER
2. Introduction to Artificial Intelligence on Amazon Web Services 3. Anatomy of a Modern AI Application 4. Section 2: Building Applications with AWS AI Services
5. Detecting and Translating Text with Amazon Rekognition and Translate 6. Performing Speech-to-Text and Vice Versa with Amazon Transcribe and Polly 7. Extracting Information from Text with Amazon Comprehend 8. Building a Voice Chatbot with Amazon Lex 9. Section 3: Training Machine Learning Models with Amazon SageMaker
10. Working with Amazon SageMaker 11. Creating Machine Learning Inference Pipelines 12. Discovering Topics in Text Collection 13. Classifying Images Using Amazon SageMaker 14. Sales Forecasting with Deep Learning and Auto Regression 15. Section 4: Machine Learning Model Monitoring and Governance
16. Model Accuracy Degradation and Feedback Loops 17. What Is Next? 18. Other Books You May Enjoy

Deploying the Contact Organizer to AWS

The deployment steps for the Contact Organizer application are similar, but slightly different from, the deployment steps in the previous projects we covered. Let's get started:

  1. For the Contact Organizer, we need to add additional Python packages to the AWS Lambda environment. We do this by adding two packages to the requirements.txt file:
    • The usaddress package is used to parse the various parts of the address, such as city, state, zip, and so on.
    • The boto3 package is specified here because we need a specific version. At the time of writing, the boto3 version in the AWS Lambda environment does not support the comprehendmedical service; we need a newer version for this project:
usaddress==0.5.10
boto3==1.9.224
  1. Next, let's tell Chalice to perform a policy analysis for us by setting "autogen_policy" to false in the config...
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