Artificial Intelligence and Big Data Trends

• 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

7 LiveLab in this lesson — see the labs panel →

• 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

2 LiveLab in this lesson — see the labs panel →

• 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

3 LiveLab in this lesson — see the labs panel →

• 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

2 LiveLab in this lesson — see the labs panel →

• 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

6 LiveLab in this lesson — see the labs panel →