Calibrate and Serve Confident AI Predictions

Building trustworthy AI requires more than accurate predictions—it requires confidence scores that genuinely reflect reality. In this short, hands-on course, you will learn how to evaluate and improve model calibration, apply temperature scaling to produce reliable confidence estimates, and deploy a scalable batch-inference pipeline using AWS Lambda. Through practical exercises, you will compute calibration metrics, visualize reliability diagrams, and integrate calibrated predictions into a serverless architecture that automatically processes incoming data and stores results for analytics. By the end of the course, you will be able to design inference workflows that are reproducible, auditable, and ready for real-world decision-making. These skills help bridge the gap between model development and production deployment, enabling you to deliver AI systems that teams can understand, trust, and use confidently

• Design reproducible and auditable inference workflows ready for decision-making
• Evaluate and improve model calibration for reliable confidence estimates
• Apply temperature scaling to enhance confidence accuracy
• Deploy scalable batch inference pipelines using AWS Lambda
• Integrate calibrated predictions into serverless architectures
• Compute and analyze calibration metrics and reliability diagrams

• Basic familiarity with the topic and common terminology
• Readiness to practice through applied exercises or case studies