AWS: ML Workflows with SageMaker, Storage & Security

AWS: ML Workflows with SageMaker, Storage & Security is the fourth course in the Exam Prep (MLA-C01): AWS Certified Machine Learning Engineer – Associate Specialization. This course enables learners to design secure, scalable, and efficient machine learning workflows on AWS, focusing on key pillars: data storage, model development, and security. Learners will begin by exploring how to collect, store, and stream ML data using services like Amazon S3, Amazon Kinesis, and Amazon Redshift. The course then transitions into hands-on model development with Amazon SageMaker, including data preparation, training, and deployment processes. In the final module, learners are introduced to the critical aspects of security and data protection, learning how to secure ML pipelines using IAM, KMS, encryption, and network controls. This course prepares learners to build production-grade ML systems that not only scale efficiently but also meet enterprise-level compliance and security requirements. This course consists of three comprehensive modules, each divided into focused lessons and practical demonstrations. Learners will gain approximately 3–3.5 hours of video content, featuring step-by-step tutorials using AWS services and real-world ML pipeline examples. Graded and Ungraded Quizzes are included in every module to test knowledge and practical readiness. Module 1: Data Storage & Real-Time Streaming on AWS Module 2: Data Preparation & ML Model Development with Amazon SageMaker Module 3: Security, Identity & Data Protection on AWS By the end of this course, learners will be able to: Design end-to-end ML workflows using AWS storage, compute, and ML services Process streaming and batch data sources for ML model development Secure ML pipelines using IAM, encryption, and network controls Build compliance-ready ML solutions using Amazon SageMaker and supporting services This course is ideal for cloud developers, ML engineers, and data professionals with hands-on experience in