Model Development & Training: Building and refining ML models using frameworks like TensorFlow, PyTorch, and Scikit-learn within SageMaker Studio .
Data Engineering & Labeling: Designing automated data pipelines and managing high-quality datasets using tools like SageMaker Ground Truth and SageMaker Data Wrangler .
Operationalizing ML (MLOps): Implementing CI/CD for machine learning through SageMaker Pipelines , automating model retraining, and managing model versions in the SageMaker Model Registry .
Deployment & Inference: Deploying models for real-time or batch inference and managing multi-model endpoints to ensure low latency and high availability.
Performance Monitoring: Using SageMaker Model Monitor and Clarify to track model quality, detect bias, and identify feature drift in production.