Model Serialisation

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 Serving

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

An optimisation algorithm that iteratively adjusts parameters in the direction of steepest descent of the loss function.

A hyperparameter that controls how much model parameters are adjusted with respect to the loss gradient during training.

A technique that decomposes a matrix into constituent matrices, widely used in recommendation systems and dimensionality reduction.

The algorithm for computing gradients of the loss function with respect to network weights, enabling neural network training.

Identifying data points, events, or observations that deviate significantly from the expected pattern in a dataset.

Synthetic Minority Over-sampling Technique — a method for addressing class imbalance by generating synthetic examples of the minority class.

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

A versioned catalogue of trained machine learning models with metadata, lineage, and approval workflows, enabling reproducible deployment and governance at enterprise scale.