1.4. Overview of AWS Machine Learning Services

The AWS Machine Learning stack is broad and comprehensive, offering services at three levels: AI Services (high-level APIs), ML Services (like Amazon SageMaker for custom model building), and ML Frameworks/Infrastructure (for deep control). For ML specialists, a deep understanding of these components is fundamental to designing efficient, scalable, and compliant ML solutions.

The First Principle of the AWS ML Landscape is to provide a comprehensive, integrated, and scalable ecosystem, enabling rapid innovation and deployment of ML solutions for all skill levels, from high-level API consumption to custom model development and MLOps. This allows ML specialists to select the right tool for every stage of the ML workflow.

This section provides a high-level overview of the core components of the AWS ML Landscape from an ML perspective: foundational compute/storage, Amazon SageMaker and its components, and various AI Services.

Scenario: You are a solutions architect tasked with choosing the right set of AWS services for a new ML initiative that includes data ingestion, model training, and real-time inference, catering to both developers and data scientists.

Reflection Question: How does understanding the different layers of the AWS ML stack (AI Services, ML Services, ML Frameworks/Infrastructure) fundamentally help you design a comprehensive and optimized ML solution that leverages the right tool for each stage of the ML workflow?