Artificial Intelligence on Amazon Web Services

(AI-AWS.AJ1) / ISBN: 978-1-64459-215-1
This course includes
Lessons
TestPrep
LiveLab
Mentoring (Add-on)
Try this course Pre-Assessment and first two Lessons free No credit card required
Are you an instructor? Teach using uCertify products
Request a free evaluation copy

Artificial Intelligence on Amazon Web Services

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.
Here's what you will get

Lessons
  • 14+ Lessons
  • 100+ Quizzes
  • 100+ Flashcards
  • 100+ Glossary of terms
TestPrep
  • 50+ Pre Assessment Questions
  • 50+ Post Assessment Questions
LiveLab
  • 18+ LiveLab
Here's what you will learn
Download Course Outline
  • Who this course is for
  • What this course covers
  • Conventions used
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Walking through convolutional neural and residual networks
  • Classifying images through transfer learning in Amazon SageMaker
  • Performing inference through Batch Transform
  • Summary
  • Further reading
  • Technical requirements
  • Understanding traditional time series forecasting
  • How the DeepAR model works
  • Understanding model sales through DeepAR
  • Predicting and evaluating sales
  • Summary
  • Further reading
  • 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
  • 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

Hands on Activities (Live Labs)

  • 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
  • Developing an AI Application Locally and a Demo Application Web User Interface
  • Hosting an S3 Static Website
  • Using Amazon Translate
  • Using Amazon Transcribe and Polly
  • Creating an Amazon DynamoDB Table
  • Using Amazon Comprehend
  • Using Amazon Lex to Build a Chat Box
  • Creating a Model
  • Using AWS Glue
  • Using Amazon SageMaker Notebook Instance
  • Building and Training a Machine Learning Model
  • Creating an Endpoint Configuration
  • Using Lifecycle Configurations in SageMaker
×