Concept drift
Concept drift is the change over time in the statistical relationship between a model's inputs and its target, formally when the joint distribution P(X, Y) (and in the most damaging case the conditional P(Y |…
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Dynamic model
A dynamic model in machine learning is a model that is retrained frequently or continuously as new data arrives, so that its parameters track changes in the underlying data distribution over time.
Inference
In machine learning, inference is the execution of a trained model to produce an output from an input.
Kubeflow
Kubeflow is an open-source MLOps platform that runs the entire machine learning lifecycle on Kubernetes, described by its creators as a project "dedicated to making using ML stacks on Kubernetes easy, fast and…
MLflow
MLflow is an open-source platform for managing the end-to-end machine learning lifecycle, covering experiment tracking, model packaging, a model registry, deployment, and (since 2025) generative-AI…
Neural architecture search
Neural architecture search (NAS) is a technique for automating the design of neural network architectures.
Pipeline
A pipeline in machine learning is a sequence of data processing steps chained together into an automated, end-to-end workflow, where each step takes the previous step's output as its input.
Pipelining
Pipelining is a term used in two distinct senses within machine learning and artificial intelligence.
Serving
In machine learning, serving (also called model serving or inference serving) is the process of deploying a trained model into a production environment so it can answer prediction requests, usually exposed as…
Training-Serving Skew
Training-serving skew is a difference between a machine learning model's performance during training and its performance during serving (production inference).