Amazon SageMaker Studio for Data Scientists

This course prepares experienced data scientists to use Amazon SageMaker Studio to accelerate every stage of the machine learning lifecycle. Participants learn how to prepare data, build, train, tune, deploy, and monitor ML models using the integrated tools and workflows provided by SageMaker Studio. Through hands-on labs and demonstrations, the course emphasizes productivity, automation, governance, and best practices for building production-ready ML solutions. By the end of the course, learners will be able to design and operate end-to-end ML workflows efficiently using SageMaker Studio and related AWS services.

• Launch and navigate Amazon SageMaker Studio and its integrated development environment
• Prepare, clean, visualize, and validate data using SageMaker Data Wrangler and Processing
• Perform feature engineering and manage features using SageMaker Feature Store
• Develop, tune, and evaluate ML models using SageMaker Studio tools
• Track experiments and model iterations using SageMaker Experiments
• Detect training issues, bias, and fairness concerns using SageMaker Debugger and Clarify
• Register, manage, and deploy models using the SageMaker Model Registry
• Design and automate end-to-end ML workflows using SageMaker Pipelines
• Implement real-time and batch inference solutions
• Monitor deployed models for data drift, bias drift, and model quality issues using SageMaker Model Monitor
• Manage SageMaker Studio resources to control usage and costs

• Experienced data scientists working with machine learning and deep learning models
• ML practitioners responsible for building, training, and deploying models on AWS
• Data professionals seeking to streamline ML workflows using SageMaker Studio
• Engineers supporting production ML systems in the AWS cloud

• Completion of AWS Technical Essentials or equivalent AWS experience
• Strong background in machine learning and deep learning fundamentals
• Proficiency in Python and common ML frameworks
• Experience building, training, tuning, and deploying ML models