uCertify

Artificial Intelligence and Big Data Trends

(UOP-CSS440.AJ2)
Lessons
Lab
TestPrep
Get A Free Trial

Skills You’ll Get

Download Course Outline

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

Any questions?
Check out the FAQs

Still have unanswered questions and need to get in touch?

Contact Us Now

Related Courses

All Courses
We use cookies for improving site experience and analytics. Learn More