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Artificial Intelligence on Amazon Web Services (AWS) Training

Take our artificial intelligence on Amazon Web Services (AWS) training course to learn fundamentals, AWS services, and practical skills to advance your career.

(AI-AWS.AJ1) / ISBN : 978-1-64459-215-1
This course includes
Lessons
TestPrep
Hands-On Labs
AI Tutor (Add-on)
298 Review
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About This Course

This Amazon AI training course trains you to build and deploy smart applications on the AWS cloud. We’ll cover everything from the basics of machine learning (ML) and deep learning to hands-on projects with AWS AI services like Rekognition, Polly, Lex, SageMaker, Translate, Transcribe, and Comprehend. You’ll learn how to design AI architectures, tackle topic modeling and image classification, and master model monitoring.

Skills You’ll Get

  • Understand concepts of ML, deep learning, and natural language processing (NLP)
  • Utilize AWS AI services, including Rekognition, Translate, Transcribe, Polly, Comprehend, Lex, SageMaker
  • Apply topic modeling techniques like Neural Topic Model 
  • Classify images using convolutional neural networks and transfer learning 
  • Forecast time series data using DeepAR models 
  • Build and deploy ML inference pipelines using SageMaker 
  • Achieve optimal model performance through hyperparameter tuning
  • Develop and deploy AI applications from scratch 
  • Manage and optimize costs on AWS

Get the support you need. Enroll in our Instructor-Led Course.

Interactive Lessons

14+ Interactive Lessons | 100+ Quizzes | 100+ Flashcards | 100+ Glossary of terms

Gamified TestPrep

50+ Pre Assessment Questions | 50+ Post Assessment Questions |

Hands-On Labs

18+ LiveLab | 00+ Minutes

Download Course Outline

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

2

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
3

Anatomy of a Modern AI Application

  • Developing an AI Application Locally and a Demo Application Web User Interface
  • Hosting an S3 Static Website
4

Detecting and Translating Text with Amazon Rekognition and Translate

  • Using Amazon Translate
5

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

  • Using Amazon Transcribe and Polly
6

Extracting Information from Text with Amazon Comprehend

  • Creating an Amazon DynamoDB Table
  • Using Amazon Comprehend
7

Building a Voice Chatbot with Amazon Lex

  • Using Amazon Lex to Build a Chat Box
8

Working with Amazon SageMaker

  • Creating a Model
9

Creating Machine Learning Inference Pipelines

  • Using AWS Glue
10

Discovering Topics in Text Collection

  • Using Amazon SageMaker Notebook Instance
  • Building and Training a Machine Learning Model
  • Creating an Endpoint Configuration
12

Sales Forecasting with Deep Learning and Auto Regression

  • Using Lifecycle Configurations in SageMaker

Any questions?
Check out the FAQs

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This course includes several hands-on activities to reinforce learning and provide practical experience. Here are some examples:

  • Build a voice chatbot with Amazon Lex 
  • Create machine learning inference pipelines using SageMaker 
  • Discover topics and patterns in text collections using the Neural Topic Model 
  • Classify images using Amazon SageMaker 
  • Sales forecasting with Deep Learning and Auto Regression

It’s recommended that you have a basic understanding of programming concepts and be familiar with cloud computing. If you are new to AI or ML, you may find it helpful to review some foundational concepts before starting the course. 

Yes, this course is well-suited for individuals seeking to prepare for job roles in AI and cloud computing. 

This course stands out due to its focus on practical application and integration with AWS services. It offers an extensive curriculum that covers key AI topics. 

Yes, you can take the AWS Certified AI Practitioner (AIF-C01) exam after completing this course.

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