Learn AI online with the Artificial Intelligence on Amazon Web Services course and lab. The lab is cloud-based, device-enabled, and can easily be integrated with an LMS. The AWS training course and lab cover some important topics in AI, such as image recognition, natural language processing, and speech recognition, and also provide a high-level understanding of AWS's AI and machine learning services and platforms. The course will guide you through the process of setting up Python, the AWS SDK, and web development tools.

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Learning Objectives

Learn AI online with the Artificial Intelligence on Amazon Web Services course and lab. The lab is cloud-based, device-enabled, and can easily be integrated with an LMS. The AWS training course and lab cover some important topics in AI, such as image recognition, natural language processing, and speech recognition, and also provide a high-level understanding of AWS's AI and machine learning services and platforms. The course will guide you through the process of setting up Python, the AWS SDK, and web development tools.

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

Audience

AWS professionals looking to leverage AI will benefit from this course.

Language

English

Prerequisites

While there are no prerequisites for this course, please ensure you have the right level of experience to be successful in this training.

Length: 365.0 days ( hours)

Level:

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