The Exam Prep: AWS Certified Machine Learning Engineer – Associate specialization is designed for professionals seeking to build and deploy scalable, production-grade machine learning solutions using Amazon Web Services (AWS). This comprehensive learning path empowers learners with practical, hands-on experience in developing, optimizing, and operationalizing ML workflows using services like Amazon SageMaker, AWS Glue, Amazon S3, AWS AI/ML APIs, and more.
Each course builds technical proficiency and exam readiness for the AWS Certified Machine Learning – Associate certification.
AWS: Machine Learning & MLOps Foundations
AWS: Feature Engineering, Data Transformation & Integrity
AWS: Model Training, Optimization & Deployment
AWS: ML Workflows with SageMaker, Storage & Security
AWS: Managed AI Services
Each course is divided into Modules, Lessons, and Video Lectures, delivering a blend of 4 to 4.5 hours of hands-on and theoretical content. Learners can assess their progress with Practice and Graded Assignments after each module, reinforcing core concepts and real-world readiness.
By completing this specialization, learners will be able to:
Design, implement, and automate scalable machine learning workflows on AWS
Prepare and transform data for ML using AWS Glue, DataBrew, and Feature Store
Train, tune, evaluate, and deploy models using Amazon SageMaker
Übungsprojekt
In Exam Prep: AWS Certified Machine Learning Engineer – Associate specialization, learners work on real-world ML projects such as fraud detection, churn prediction, and recommendation systems. Using Amazon SageMaker and AWS tools, they practice data preparation, feature engineering, model training, tuning, and deployment.
These hands-on labs reinforce key MLOps and ML concepts, helping learners build and manage end-to-end ML workflows in a production-ready AWS environment.
