Amazon SageMaker Fundamentals
Description
Comprehensive introduction to Amazon SageMaker platform, its core components, and basic ML workflow implementation.
Learning Objectives
Learners will master Amazon SageMaker fundamentals including platform architecture, development environments, notebook instances, and basic ML workflow implementation. They will understand SageMaker Studio, data access patterns, built-in algorithms, and integration with other AWS services, establishing a strong foundation for advanced ML engineering tasks on the AWS platform.
Topics (10)
Comprehensive introduction to SageMaker including platform overview, service components, pricing model, and integration with AWS ecosystem.
Detailed exploration of SageMaker Studio including IDE features, notebook management, collaboration tools, and development workflow optimization.
Comprehensive coverage of data access including S3 integration, data formats, input modes (File vs Pipe), and data security considerations.
Detailed overview of SageMaker built-in algorithms including XGBoost, Linear Learner, K-means, PCA, and their practical applications.
Practical training job configuration including instance types, hyperparameters, algorithm selection, and training optimization techniques.
Comprehensive model deployment including endpoint creation, inference configuration, auto-scaling, and deployment best practices.
Advanced SDK usage including training job management, model deployment automation, and integration with other AWS services through APIs.
Container fundamentals including Docker basics, custom container creation, ECR integration, and containerized ML model deployment.
Advanced troubleshooting techniques, performance optimization, error handling, logging, and best practices for scalable ML implementation.
Practical guide to notebook instance configuration, Jupyter environments, kernel management, and integration with data sources and compute resources.