Model Monitoring

AUC Score

The AUC score, or Area Under the ROC Curve, is a classification evaluation metric that measures how well a model distinguishes between positive and negative cl…

Data Labeling

Data labeling is the process of assigning informative tags, categories, or ground-truth values to raw data so it can be used to train supervised machine learni…

Data Annotation

Data annotation is the process of enriching raw data with structured metadata — labels, tags, transcriptions, relationships, or attributes — so it can be used…

Human-in-the-Loop

Human-in-the-loop (HITL) is a machine learning design pattern in which human judgment is deliberately incorporated into a model's training, evaluation, or infe…

Model Serving

Model serving is the process of deploying a trained machine learning model so it can receive input data and return predictions in a production environment, typ…

Model Monitoring

Model monitoring is the ongoing practice of tracking a deployed machine learning model's performance, input data characteristics, and predictions in production…

Concept Drift

Concept drift is the phenomenon where the statistical relationship between a model's input features and its target output changes over time, causing a previous…

A/B Testing (ML)

A/B testing in machine learning is a controlled experimentation method that compares two or more model versions by routing production traffic between them and…

Shadow Deployment

Shadow deployment is a model release strategy in which a new model version runs in parallel with the production model on live traffic, generating predictions t…

Canary Model Deployment

Canary model deployment is a gradual rollout strategy in which a new model version is exposed to a small percentage of production traffic first, with exposure…