4.2.3. Amazon Sagemaker (Developer Interaction)

First Principle: Amazon SageMaker empowers developers to build, train, and deploy machine learning (ML) models at scale, abstracting the underlying infrastructure complexity of the ML lifecycle.

SageMaker handles the full ML lifecycle. For DVA-C02, focus on how developers invoke SageMaker inference endpoints from application code. It simplifies the end-to-end ML workflow, allowing developers to focus on the ML problem.

• SageMaker Studio: A web-based IDE for the entire ML workflow.
• Built-in Algorithms & Frameworks: Provides pre-built ML algorithms and supports popular frameworks like TensorFlow and PyTorch.
• Managed Training: Automates the provisioning and management of compute resources for training ML models at scale.
• Model Deployment: Easily deploy ML models as highly available and scalable API endpoints for real-time inference or batch transformations.
• SageMaker SDK for Python: Developers can interact with SageMaker programmatically from their Python code to define, train, and deploy ML models.
• MLOps Tools: Basic support for automating ML workflows.

Scenario: You're developing an application that needs to integrate with a machine learning model for real-time predictions. You need to train this model on a large dataset and deploy it as a scalable API endpoint without managing complex ML infrastructure.

⚠️ Exam Trap: The DVA-C02 only tests SageMaker from a developer integration perspective — how to invoke an endpoint, not how to train models. If a question asks about ML model training details, it's outside DVA-C02 scope.