AI Glossary
The complete dictionary of Artificial Intelligence
MLOps
Engineering practice that aims to deploy and maintain machine learning models in production reliably and efficiently.
Model monitoring
Continuous monitoring of model performance and behavior in production to detect anomalies.
Training pipeline
Automated sequence of steps to prepare data, train and validate ML models.
Continuous deployment
Automation of the process of deploying ML models to production after successful validation.
Model versioning
Systematic management of different versions of an ML model with associated metadata.
A/B testing for models
Methodology to compare the performance of multiple models in production simultaneously.
ML cascade
Architecture where multiple models are executed in sequence, each model refining the results of the previous one.
ML observability
Ability to measure, understand and diagnose the internal behavior of ML systems in production.
Model auto-scaling
Automatic adjustment of computing resources based on model prediction load.