Amazon Web Services Machine Learning Essential Training

Learn about patterns, services, processes, and best practices for designing and implementing machine learning using Amazon Web Services.

Introduction

• Welcome
• About using cloud services

1. Machine Learning on AWS

• AWS Machine Learning concepts
• Business scenarios for machine learning
• Which algorithm should I use?
• AWS AI servers vs. platforms
• AWS AI platforms vs. frameworks
• A classifier in action: Amazon Macie

2. Machine Learning API Services

• Setup for AWS machine learning APIs
• Predict using AWS Comprehend for NLP
• Predict using AWS Polly text-to-speech
• Predict using AWS Lex for chatbots
• Predict using AWS Rekognition for images
• Predict using AWS Rekognition for video
• Predict using Transcribe and Translate

3. Machine Learning Platforms

• Understanding ML platforms
• Understanding and using AWS Machine Learning
• Understanding SageMaker
• Create Jupyter notebooks with SageMaker
• Get data with SageMaker notebook
• Train model with SageMaker job
• Deploy and host model with SageMaker model
• Use model from SageMaker endpoint
• Selecting algorithm for model training
• Advanced use of SageMaker

4. Machine Learning Virtual Servers

• Understanding ML virtual servers
• Understanding deep learning
• Work with Gluon for MXNet in SageMaker
• Work with MXNet in SageMaker
• Databricks on AWS
• Work with MXNet in Databricks
• Set up the AWS Deep Learning AMIs
• Work with the AWS Deep Learning AMI
• Work with EMR for machine learning

5. Machine Learning Architectures

• AWS ML APIs for conversational apps
• AWS ML service for IoT apps
• Spark ML and Databricks AWS for real-time apps
• VariantSpark and EMR for genomic research
• Best practices for algorithms and architectures

Conclusion

• Next steps