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 a...

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