AI, Machine Learning and Service Platforms

• 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

2 LiveLab in this lesson — see the labs panel →

• 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

1 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
• Summary
• Further reading

1 LiveLab in this lesson — see the labs panel →

• 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

1 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
• Summary
• Further reading

3 LiveLab in this lesson — see the labs panel →

• 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

1 LiveLab in this lesson — see the labs panel →

• 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