What does this AWS training cover in the context of machine learning pipelines?
This course covers the full machine learning pipeline on AWS using Amazon SageMaker. Each phase of the pipeline—from data preparation to deployment—is taught through hands-on exercises designed to build real-world machine learning models in the AWS Cloud.
How will I learn to use Amazon SageMaker for ML workflows?
You’ll work directly with Amazon SageMaker features in guided labs and exercises. You’ll learn how to automate ML workflows, train and evaluate models, and manage endpoints for scalable deployment—all using SageMaker tools and environments.
Will I learn to deploy an ML model using SageMaker?
Yes. Model deployment is a major focus of the course. You’ll learn how to deploy an ML model using Amazon SageMaker endpoints, manage inference, and apply best practices for secure, cost-optimized deployment in the AWS Cloud.
Does this course include hands-on exercises for real ML projects like fraud detection?
Absolutely. You’ll select one of three business use cases—fraud detection, recommendation engines, or flight delays—and complete hands-on exercises that apply every phase of the ML pipeline using Amazon SageMaker.
What makes this AWS course valuable for professionals working with machine learning models?
This course offers a complete learning experience—from instructor presentations to hands-on labs—focused on solving real business problems using SageMaker. You’ll gain practical experience in model development, evaluation, and deployment in the AWS Cloud.