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.