Machine Learning with AWS Sagemaker -- DWIDC5532895

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. Optimization:

Tuning hyperparameters and optimizin...