Applied AI · 2022
AI/ML Video Confidence Predictor
Growth Phase: Modernization and Scale
Computer-vision workflow for confidence scoring from video signals.
AWS SageMakerMediaPipeMoviePyLambdaStep FunctionsComputer Vision
Architecture Responsibility
Responsible for technology architecture and hands-on delivery direction across system design, deployment, DevOps, cost, scale, reliability, and production readiness.
Outcome
Automated facial and posture expression analysis to derive High, Medium, and Low confidence scores.
Scale
Built as an automated video-analysis pipeline for repeatable confidence scoring.
Architecture
- Used AWS SageMaker for model experimentation and ML workflow support.
- Used MediaPipe and MoviePy for video processing and feature extraction.
- Automated processing with AWS Lambda and Step Functions.
- Designed the flow to convert raw video inputs into structured confidence classifications.
Lessons Learned
- Applied AI systems need deterministic preprocessing and clear scoring logic so model outputs remain explainable and operationally useful.
- Serverless orchestration works well for staged AI/ML workflows when each processing step has clear inputs, outputs, and failure handling.
- AI architecture should make the business signal visible, not just run a model and return a score.