uCertify

AI, Machine Learning and Service Platforms

(DV-CIS309.AJ1)
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1

Preface

  • Who this course is for
  • What this course covers
  • Conventions used
2

Introduction to Artificial Intelligence on Amazon Web Services

  • 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

  • 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

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
5

Performing Speech-to-Text and Vice Versa with Amazon Transcribe and Polly

  • 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
6

Extracting Information from Text with Amazon Comprehend

  • 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
7

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
8

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
9

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
10

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
11

Classifying Images Using Amazon SageMaker

  • Walking through convolutional neural and residual networks
  • Classifying images through transfer learning in Amazon SageMaker
  • Performing inference through Batch Transform
  • Summary
  • Further reading
12

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
13

Model Accuracy Degradation and Feedback Loops

  • 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
14

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

1

Introduction to Artificial Intelligence on Amazon Web Services

  • Using the Amazon Rekognition Service
  • Creating an Amazon S3 Bucket
  • Installing Python on Linux
  • Installing Python on Windows
  • Creating a Python Virtual Environment and Project with the AWS SDK
2

Anatomy of a Modern AI Application

  • Developing an AI Application Locally and a Demo Application Web User Interface
  • Hosting an S3 Static Website
3

Detecting and Translating Text with Amazon Rekognition and Translate

  • Using Amazon Translate
4

Performing Speech-to-Text and Vice Versa with Amazon Transcribe and Polly

  • Using Amazon Transcribe and Polly
5

Extracting Information from Text with Amazon Comprehend

  • Creating an Amazon DynamoDB Table
  • Using Amazon Comprehend
6

Building a Voice Chatbot with Amazon Lex

  • Using Amazon Lex to Build a Chat Box
7

Working with Amazon SageMaker

  • Creating a Model
8

Creating Machine Learning Inference Pipelines

  • Using AWS Glue
9

Discovering Topics in Text Collection

  • Using Amazon SageMaker Notebook Instance
  • Building and Training a Machine Learning Model
  • Creating an Endpoint Configuration
10

Sales Forecasting with Deep Learning and Auto Regression

  • Using Lifecycle Configurations in SageMaker

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