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 optimizing training costs using Managed Spot Training and distributed training libraries. Essential Skills & Qualifications AWS Expertise: Proficiency in Amazon SageMaker and related services such as S3, Lambda, IAM, and Step Functions. Programming: Strong command of Python (specifically the SageMaker Python SDK) or R, and SQL. ML Frameworks: Deep experience with modern libraries including PyTorch, TensorFlow, and XGBoost. Mathematical Foundation: Solid understanding of statistics, linear algebra, and predictive modeling. Cloud Infrastructure: Experience managing compute clusters, VPCs, and ensuring security best practices. #J-18808-Ljbffr