AWS ML Services
A practical guide to AWS services used in machine learning workflows, from data preparation to model deployment and monitoring. This section covers the full spectrum of AWS offerings that support ML workloads — from the core SageMaker platform to specialized AI APIs, data engineering tools, and operational services.
How This Section Is Organized
Each page covers a logical grouping of services, explains what they do, when to use them, and includes flashcards to reinforce key concepts.
Service Categories
- SageMaker - The core ML platform
- SageMaker Features - Deep dive into SageMaker capabilities
- Built-in Algorithms - Pre-built ML algorithms
- AI Application Services - Ready-to-use AI APIs
- Streaming and Real-Time - Real-time data processing
- ETL and Data Processing - Data transformation pipelines
- Storage - Data storage for ML
- Compute and Containers - Training and inference infrastructure
- Analytics and Query - Data analysis tools
- Security and Governance - ML security and compliance
- Monitoring and Orchestration - ML operations
- Edge and IoT - ML at the edge
- Decision Trees - When to use what