Coursera MOOC / Non-credit 0

Calibrate and Serve Confident AI Predictions

Name: Calibrate and Serve Confident AI Predictions
Price: 49 USD
Availability: InStock

About this course

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.

63/100

CourseAsk score

What the provider tells you: 39/45
Who stands behind it: 8/35
How complete the listing is: 16/20

Scores how much the provider publishes and who stands behind it — not how well it is taught.

What you'll learn

evaluate model calibration
improve confidence scores
apply temperature scaling
compute calibration metrics
visualize reliability diagrams
integrate calibrated predictions into a serverless architecture

Course objectives

design reproducible inference workflows
create auditable AI systems

Machine Learning Artificial Intelligence #data processing #model calibration #ai #aws lambda #serverless architecture #batch inference #confidence scores #temperature scaling #reliability diagrams #calibration metrics

$49.00

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