AI Deployment Basics
A Quantized AI Model Needs Its Own Quality Check
Evaluate a quantized AI model as a separate deployment choice, measuring memory, task quality and compatibility instead of relying on the bit-width label.
AI · VISION · EVERYDAY SYSTEMS
AI evaluation
Understanding Models
A Model Card Helps You Ask What an AI System Was Tested For
Read an AI model card for intended uses, evaluation conditions and limitations, then identify what still needs testing in your own application.
Practical AI
A Benchmark Average Can Hide the Language Your Users Need
A model’s overall benchmark result may hide weak performance in a language your users rely on. Evaluate the actual task and language mix separately.
AI reliability
Ask Which Error Matters Before Comparing AI Accuracy
Use a simple confusion-matrix example to see why a high accuracy score can hide the AI errors that matter most for the task.
AI reliability
A Confident AI Answer Needs an Abstain Option
Design an AI workflow that can leave a question unresolved, request evidence or escalate instead of rewarding a fluent answer to every input.