Amazon SageMaker Studio for Data Scientists

Amazon SageMaker Studio for Data Scientists is an advanced, three-day course designed for experienced data scientists who want to use Amazon SageMaker Studio across the machine learning lifecycle. The course covers data preparation, model development, training, tuning, deployment, inference, monitoring, and resource management.

Participants work with SageMaker capabilities including Data Wrangler, Amazon EMR, SageMaker Processing, Feature Store, Experiments, Debugger, Clarify, Model Registry, Pipelines, and Model Monitor. The course combines demonstrations, practice labs, discussions, and a capstone project focused on building an end-to-end tabular machine learning solution using SageMaker Studio and the SageMaker Python SDK.

• Launch and navigate Amazon SageMaker Studio.
• Prepare, clean, visualize, analyze, and transform data using SageMaker Studio.
• Implement repeatable data-processing workflows and validate data for machine learning readiness.
• Apply feature engineering using SageMaker Feature Store.
• Develop, tune, and evaluate machine learning models using SageMaker capabilities.
• Track training and tuning iterations using SageMaker Experiments and identify development issues using SageMaker Debugger.
• Analyze bias and explainability considerations using SageMaker Clarify.
• Register, manage, deploy, and infer models using SageMaker Model Registry and related deployment capabilities.
• Automate end-to-end machine learning workflows using SageMaker Pipelines.
• Monitor data quality, model quality, bias drift, and feature-attribution drift using SageMaker Model Monitor.

• Experience with machine learning and deep learning fundamentals.
• Experience using machine learning frameworks and Python programming.
• Experience building, training, tuning, and deploying machine learning models.
• AWS Technical Essentials is recommended before attending the course.
• Learners without data science experience are recommended to complete The Machine Learning Pipeline on AWS and Deep Learning on AWS, followed by relevant hands-on experience building models.