Amazon SageMaker Studio for Data Scientists
About This Course
This advanced-level 3-day (24-hour) course equips experienced data scientists with the end-to-end computational modelling and analytical skills required to build, train, deploy, and monitor machine learning solutions using Amazon SageMaker Studio. Through a blend of technical presentations, hands-on labs, interactive demonstrations, and a comprehensive capstone project, participants learn to streamline every phase of the machine learning lifecycle.
Participants will leverage SageMaker Studio’s integrated suite—including Data Wrangler, Feature Store, Experiments, Debugger, Clarify, Pipelines, and Model Monitor—to perform scalable data processing, automate model tuning, enforce fairness, and deploy scalable inference endpoints in enterprise production environments.
What You'll Learn
• LO1 - Configure and navigate the Amazon SageMaker Studio environment to manage notebooks, extensions, and studio resources.
• LO2 - Perform end-to-end data processing, feature engineering, and analytics using SageMaker Data Wrangler, Amazon EMR, AWS Glue, SageMaker Processing, and SageMaker Feature Store.
• LO3 - Construct, train, and optimize machine learning models using custom scripts, built-in algorithms, automated ML (SageMaker Autopilot), and SageMaker Experiments.
• LO4 - Analyze model performance, debug training runs, and evaluate model bias and explainability using SageMaker Debugger and SageMaker Clarify.
• LO5 - Deploy, scale, and test inference endpoints while automating ML workflows using SageMaker Pipelines and SageMaker Model Registry.
• LO6 - Implement continuous monitoring for data and model quality, and manage SageMaker Studio resources to control operational costs.
Entry Requirements
• Python programming experience
• At least 1 year of experience as a data scientist responsible for training, tuning, and deploying models