MLOps and CI/CD for Machine Learning
Description
Comprehensive MLOps practices, CI/CD pipeline implementation, and automation strategies for machine learning workflows on AWS.
Learning Objectives
Learners will master MLOps principles and practices including CI/CD pipeline design for ML workflows, automated testing strategies, model versioning, and deployment automation. They will understand how to implement end-to-end ML pipelines using SageMaker Pipelines, integrate with code repositories, implement automated retraining, and establish governance frameworks for production ML systems.
Topics (12)
Comprehensive MLOps foundation including DevOps principles for ML, organizational structures, process automation, and cultural transformation for ML engineering teams.
Advanced CI/CD pipeline design including ML-specific stages, testing strategies, deployment automation, and integration with version control systems.
Comprehensive SageMaker Pipelines implementation including pipeline definition, step orchestration, conditional execution, parameter management, and workflow optimization.
Advanced testing strategies including unit testing for ML code, data quality testing, model performance testing, and end-to-end system testing automation.
Advanced model registry usage including model registration, metadata management, version control, approval workflows, and model promotion strategies.
Advanced IaC implementation including CloudFormation templates, CDK usage, environment standardization, and automated infrastructure provisioning for ML workloads.
Comprehensive automated retraining including performance monitoring triggers, data freshness validation, automated training pipelines, and model update automation.
Advanced version control including Git workflows, branching strategies for ML projects, code review processes, and integration with CI/CD systems.
Comprehensive environment management including containerization, dependency management, environment reproducibility, and configuration management for ML systems.
Advanced feature store usage including SageMaker Feature Store implementation, feature versioning, sharing strategies, and integration with ML pipelines.
Comprehensive security implementation including secret management, access control, audit trails, compliance automation, and security scanning in ML pipelines.
Advanced governance including approval workflow design, compliance tracking, change management, documentation automation, and enterprise governance integration.