Overview
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Learn about patterns, services, processes, and best practices for designing and implementing machine learning using Amazon Web Services.
Syllabus
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Introduction
- Welcome
- About using cloud services
- 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
- 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
- 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
- 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
- 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
- Next steps
