Principle:Mlflow Mlflow Model Training
| Knowledge Sources | |
|---|---|
| Domains | ML_Ops, Model_Management |
| Last Updated | 2026-02-13 20:00 GMT |
Overview
Fitting a predictive model to data is the foundational step that produces the artifact every subsequent stage of the model lifecycle depends on.
Description
Model training is the process by which an algorithm consumes a prepared dataset and iteratively adjusts its internal parameters to minimise a loss function. The result is a fitted model object that encodes the learned patterns and is ready for evaluation, serialisation, or deployment. Because training is compute-intensive and non-deterministic, it is essential to capture the exact conditions under which a model was produced so that results can be reproduced, compared, and audited.
In a managed lifecycle platform the training step sits upstream of every other concern. The output of training -- a model object residing in memory -- becomes the input to model logging, evaluation, registration, and eventual serving. Without a well-defined training step, the downstream pipeline has nothing to persist or govern.
Training itself is framework-agnostic. Practitioners may use gradient-based optimisers, ensemble methods, nearest-neighbour algorithms, or any other learning strategy. What matters to the lifecycle platform is the contract: training accepts data and hyperparameters and produces a callable model object with a prediction interface.
Usage
Use this principle whenever a new predictive model must be created from data. It applies regardless of whether the model is a simple linear regression or a deep neural network. The training step should always be instrumented with experiment tracking so that hyperparameters, metrics, and the resulting model artifact are recorded together for later comparison.
Theoretical Basis
Model training rests on statistical learning theory. A learning algorithm selects a hypothesis from a hypothesis space that best fits the observed data according to a chosen loss function. Regularisation, cross-validation, and early stopping are common techniques used to control overfitting and ensure that the model generalises to unseen data. The bias-variance trade-off governs the complexity of the model that can be justified given the available training set size.
Hyperparameter tuning -- selecting learning rate, tree depth, number of estimators, and similar configuration values -- is itself a search problem often solved by grid search, random search, or Bayesian optimisation. Because the choice of hyperparameters directly affects model quality, recording them alongside training metrics is a core requirement of any reproducible workflow.