Machine Learning Engineering on AWS

Course subjects

Module 0: Course Introduction

Module 1: Introduction to Machine Learning (ML) on AWS

• Introduction to ML

• Amazon SageMaker AI

• Responsible ML

Module 2: Analysing Machine Learning (ML) Challenges

• Evaluating ML business challenges

• ML training approaches

• ML training algorithms

Module 3: Data Processing for Machine Learning (ML)

• Data preparation and types

• Exploratory data analysis

• AWS storage options and choosing storage

Module 4: Data Transformation and Feature Engineering

• Handling incorrect, duplicated, and missing data

• Feature engineering concepts

• Feature selection techniques

• AWS data transformation services

• Lab 1: Analyse and Prepare Data with Amazon SageMaker Data Wrangler and Amazon EMR

• Lab 2: Data Processing Using SageMaker Processing and the SageMaker Python SDK

Module 5: Choosing a Modeling Approach

• Amazon SageMaker AI built-in algorithms

• Selecting built-in training algorithms

• Amazon SageMaker Autopilot

• Model selection considerations

• ML cost considerations

Module 6: Training Machine Learning (ML) Models

• Model training concepts

• Training models in Amazon SageMaker AI

• Lab 3: Training a model with Amazon SageMaker AI

Module 7: Evaluating and Tuning Machine Learning (ML) models

• Evaluating model performance

• Techniques to reduce training time

• Hyperparameter tuning techniques

• Lab 4: Model Tuning and Hyperparameter Optimisation with Amazon SageMaker AI

Module 8: Model Deployment Strategies

• Deployment considerations and target options

• Deployment strategies

• Choosing a model inference strategy

• Container and instance types for inference

• Lab 5: Shifting Traffic A/B

Module 9: Securing AWS Machine Learning (ML) Resources

• Access control

• Network access controls for ML resources

• Security considerations for CI/CD pipelines

Module 10: Machine Learning Operations (MLOps) and Automated Deployment

• Introduction to MLOps

• Automating testing in CI/CD pipelines

• Continuous delivery services

• Lab 6: Using Amazon SageMaker Pipelines and the Amazon SageMaker Model Registry with Amazon SageMaker Studio

Module 11: Monitoring Model Performance and Data Quality

• Detecting drift in ML models

• SageMaker Model Monitor

• Monitoring for data quality and model quality

• Automated remediation and troubleshooting

• Lab 7: Monitoring a Model for Data Drift

Module 12: Course Wrap-up

Please note: This is an emerging technology course. Course outline is subject to change as needed.