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Artificial Intelligence and Big Data Trends

(UOP-CSS440.AJ1)
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1

Introduction and Anatomy of a Modern AI Application

  • Intelligence
  • What is AI?
  • How BI has developed
  • Appendix D: Comparison Between Machine Learning and Traditional Business Intelligence
  • What is learning?
  • Big Data and the Internet of Things will reshuffle the cards
  • Artificial Intelligence linked to Big Data will undoubtedly be the keystone of digital learning
  • Supervised learning
  • Enhanced supervised learning
  • Unsupervised learning
  • Overview of AWS AI offerings
  • Getting familiar with the AWS CLI
  • Using Python for AI applications
  • First project with the AWS SDK
  • 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
2

Building Applications with AWS AI Services - Part I

  • 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
  • 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
3

Building Applications with AWS AI Services - Part II

  • 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
  • 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
4

Training Machine Learning Models with Amazon SageMaker - Part I

  • 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
  • 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
5

Training Machine Learning Models with Amazon SageMaker - Part II

  • 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
  • Walking through convolutional neural and residual networks
  • Classifying images through transfer learning in Amazon SageMaker
  • Performing inference through Batch Transform
  • Technical requirements
  • Understanding traditional time series forecasting
  • How the DeepAR model works
  • Understanding model sales through DeepAR
  • Predicting and evaluating sales

1

Introduction and Anatomy of a Modern AI Application

  • 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
2

Building Applications with AWS AI Services - Part I

  • Using Amazon Translate
  • Using Amazon Transcribe and Polly
3

Building Applications with AWS AI Services - Part II

  • Creating an Amazon DynamoDB Table
  • Using Amazon Comprehend
  • Using Amazon Lex to Build a Chat Box
4

Training Machine Learning Models with Amazon SageMaker - Part I

  • Creating a Model
  • Using AWS Glue
5

Training Machine Learning Models with Amazon SageMaker - Part II

  • Using Amazon SageMaker Notebook Instance
  • Building and Training a Machine Learning Model
  • Creating an Endpoint Configuration
  • Performing Exploratory Data Analysis using AWS SageMaker
  • Deploying Machine Learning Model using AWS SageMaker
  • Using Lifecycle Configurations in SageMaker

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