Model Registry

Machine Learning

A subset of AI that enables systems to automatically learn and improve from experience without being explicitly programmed.

Supervised Learning

A machine learning paradigm where models are trained on labelled data, learning to map inputs to known outputs.

Unsupervised Learning

A machine learning approach where models discover patterns and structures in data without labelled examples.

Reinforcement Learning

A machine learning paradigm where agents learn optimal behaviour through trial and error, receiving rewards or penalties.

Multi-Task Learning

A machine learning approach where a model is simultaneously trained on multiple related tasks to improve generalisation.

Online Learning

A machine learning method where models are incrementally updated as new data arrives, rather than being trained in batch.

Batch Learning

Training a machine learning model on the entire dataset at once before deployment, as opposed to incremental updates.

Active Learning

A machine learning approach where the algorithm interactively queries a user or oracle to label new data points.

Ensemble Learning

Combining multiple machine learning models to produce better predictive performance than any single model.

Feature Selection

The process of identifying and selecting the most relevant input variables for a machine learning model.

Epoch

One complete pass through the entire training dataset during the machine learning model training process.

Model Serialisation

The process of converting a trained model into a format that can be stored, transferred, and later reconstructed for inference.

The tendency of neural networks to completely lose previously learned knowledge when trained on new tasks, a fundamental challenge in continual and multi-task learning.

Techniques that reduce the number of input variables in a dataset while preserving essential information and structure.

A training strategy that presents examples to a model in a meaningful order, typically from easy to hard.

A dimensionality reduction technique that transforms data into orthogonal components ordered by the amount of variance they explain.

The process of adjusting a model's predicted probabilities so they accurately reflect the true likelihood of outcomes, essential for risk-sensitive decision-making.

The infrastructure and processes for deploying trained machine learning models to production environments for real-time predictions.

A mathematical function that measures the difference between predicted outputs and actual target values during model training.

A tree-structured model where internal nodes represent feature tests, branches represent outcomes, and leaves represent predictions.

Governance

The system of policies, rules, and processes by which activities are directed, controlled, and managed.