Term tag: MLOps

A/B Testing

A/B testing is the randomized experiment that decides whether a model, prompt, or feature change truly improves a live metric — or just moved on noise.

Concept Drift

Concept drift is when the relationship a deployed model learned changes over time, so accuracy silently decays until you detect the drift and retrain.

Continuous Learning (CL)

Keeping a deployed model current as the world moves — why teams schedule batch retraining instead of true online learning, and what breaks if they don't.

Data Processing

What happens to raw data before a model can use it: ingestion, validation, transformation, ETL vs ELT, batch vs streaming, and what silently breaks.

LLM-as-a-Judge

Using one model to grade another's output against a rubric: how it works, the biases that break it, and the human agreement rate you can actually reach.

Machine Learning Operations (MLOps)

Why shipping a model is not like shipping software: the artifact has three inputs — code, data and trained weights — and only one of them lives in git.

Model Deployment

What has to happen between a model that trains well and users calling it: replica sizing, latency budgets, cold starts, and the rollback nobody built.

Monitoring

Why a deployed model rots while every dashboard stays green — and what you can actually measure when the true labels arrive weeks late, or never.

Production Systems

A model serving live users under latency and uptime guarantees — judged at the 99th percentile under real load, not by the median on a laptop.