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Process by which a machine learning model learns parameters from data to enable generalizable predictions.
Model training describes the process by which a machine learning model learns parameters from training data and includes data preparation, optimization, validation, hyperparameter tuning, and evaluation. Used in ML and AI pipelines, it is critical for predictive quality and readiness for production. Common challenges are overfitting, data quality, and reproducibility.
Measures prediction quality on the validation set.
Total duration of the training process per run.
CPU/GPU utilization and memory usage during training.
A batch training pipeline uses user and transaction data for personalized recommendations.
Supervised training on annotated image datasets to detect lesions.
Time-series model trained on sensor data for early failure detection.
Perform data exploration, cleaning and feature engineering.
Define and version training and validation splits.
Set up training pipeline with monitoring, checkpoints and logging.
Validate, version and register models in the registry.