Experiment Tracking

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.

A mathematical framework for modelling sequential decision-making where outcomes are partly random and partly controlled.

A regularisation technique combining L1 and L2 penalties, balancing feature selection and coefficient shrinkage.

t-Distributed Stochastic Neighbour Embedding — a technique for visualising high-dimensional data in two or three dimensions.

A learning approach that combines a small amount of labelled data with a large amount of unlabelled data during training.

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

Bootstrap Aggregating — an ensemble method that trains multiple models on random subsets of data and averages their predictions.

Uniform Manifold Approximation and Projection — a dimensionality reduction technique for visualisation and general non-linear reduction.

A regularised regression technique that adds an L1 penalty, enabling feature selection by driving some coefficients to zero.

Metrics

Quantitative measurements collected over time to track system performance, health, and business outcomes.