Model Monitoring

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 balance between a model's ability to minimise bias (error from assumptions) and variance (sensitivity to training data fluctuations).

A technique where knowledge gained from training on one task is applied to a different but related task.

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

When a model is too simple to capture the underlying patterns in the data, resulting in poor performance on both training and test data.

An ensemble technique that builds models sequentially, with each new model correcting residual errors of the combined ensemble.

An optimised distributed gradient boosting library designed for speed and performance in machine learning competitions and production.

A supervised learning algorithm that finds the optimal hyperplane to separate different classes in high-dimensional space.

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

Data Drift

Changes in the statistical properties of data over time that can degrade machine learning model performance.