AWS Core Services for Machine Learning Workflows

Building successful machine learning models requires a solid foundation in cloud infrastructure and data management. Navigating the vast ecosystem of AWS services can be overwhelming when preparing for machine learning roles or certifications. This text-based course guides you through the essential AWS core services needed to build, deploy, and manage machine learning workflows. You will understand how storage, compute, data integration, and specialized ML tools work together to create robust, production-ready pipelines. What you'll learn: 1. Understand the fundamentals of AWS storage and compute, focusing on S3 and EC2 for machine learning workloads. 2. Master data preparation and ETL pipelines using Glue to clean and transform training datasets. 3. Configure SageMaker environments to build, train, and deploy machine learning models efficiently. 4. Analyze modern MLOps concepts, security best practices, and data access controls within the AWS ecosystem. 5. Practice your knowledge with comprehensive written review questions designed to reinforce key concepts for the MLA-C01 exam. You will start with core cloud infrastructure and data storage concepts before moving into data engineering pipelines and model development with SageMaker. Finally, you will review your understanding through written scenario-based self-assessments to ensure exam readiness. This course is designed for beginners looking to understand AWS machine learning infrastructure and candidates preparing for AWS machine learning certification. No advanced cloud experience is required. Start reading today to build your cloud machine learning foundation.