Model Serving

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.

An ensemble technique that sequentially trains models, each focusing on correcting the errors of previous models.

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

A recommendation technique that makes predictions based on the collective preferences and behaviour of many users.

When a model learns the training data too well, including noise, resulting in poor performance on unseen data.

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

A clustering method that builds a tree-like hierarchy of clusters through successive merging or splitting of groups.

Density-Based Spatial Clustering of Applications with Noise — a clustering algorithm that finds arbitrarily shaped clusters based on density.

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