Amazon SageMaker for AI/ML Development
Description
Comprehensive understanding of Amazon SageMaker platform for building, training, and deploying machine learning and AI models with integrated development environment, automated ML capabilities, and production deployment features.
Learning Objectives
Learners will master SageMaker Studio IDE, data preparation and feature engineering, model training and tuning, automated ML capabilities, model deployment and endpoints, monitoring and management features, and integration with other AWS services. They will understand how to leverage SageMaker for end-to-end AI/ML workflows from development to production.
Topics (9)
Comprehensive overview of SageMaker platform, architecture, core services, and position in AWS AI/ML ecosystem.
Comprehensive study of data preparation workflows, feature engineering, data transformation, and preprocessing using SageMaker tools.
Detailed study of SageMaker training infrastructure, built-in algorithms, custom training scripts, and distributed training strategies.
Comprehensive coverage of hyperparameter optimization, tuning jobs, search strategies, and performance optimization techniques.
Study of automated ML workflows, SageMaker Autopilot, automated feature engineering, and no-code ML solutions.
Comprehensive study of deployment options, inference endpoints, scaling strategies, and production deployment best practices.
Detailed coverage of model monitoring systems, drift detection, quality metrics, alerts, and model governance practices.
Comprehensive study of AWS service integrations, data pipelines, security integration, and ecosystem connectivity.
Detailed exploration of SageMaker Studio interface, Jupyter notebooks, code management, collaboration tools, and development workflows.