Train, test, and evaluate your models with the Model product.
Type | Versioned |
---|---|
Data | Data rows, labels (ground truth), and data split |
Model | Model metrics and model configs |
Term | Definition |
---|---|
Model | A model is a large language model integrated by Foundry or your custom configuration specified by an ontology of data. |
Experiment | An experiment is a directory where you can create, manage, and compare a set of model runs related to the same machine-learning task. See Experiements . |
Model run | A model run is a model training experiment within a model directory. Each model run has its data snapshot (data rows, annotations, and data splits) versioned. You can upload predictions to a model run, and compare its performance against other model runs in the model directory. See Create a model run. |
Data split | You can split the selected data rows into train, validation, and test splits to prepare for model training and evaluation. See Splits. |
Data versioning | Each model run keeps its own versioned data snapshot. The snapshot contains the data rows, annotations, and data splits. It is immutable, meaning it remains the same even if new annotations are added or existing annotations are updated. You can export it from the model run to train or use it to reproduce a model. |
Model config (hyperparameters) | Each model run will keep a version of its model configurations (such as hyperparameters), and model type. See Model runs. |
Model training | Export the model run snapshot from Labelbox and train a model in your custom ML environment. See Export model run data (beta). |
Error analysis | Error analysis is the process through which ML teams analyze where model predictions disagree with ground truth labels. A disagreement can be a model error (poor model prediction) or a labeling mistake (ground truth is wrong). See Improve model performance. |
Slice | A slice represents a subset of your training data bound by a common characteristic. From the Model tab, you can create slices to visually inspect your training data and view model metrics reported on each slice. See Slices. |
Active learning | Active learning is the process through which ML teams identify, among all their unlabeled data, which high-value data rows they will label in priority. Labeling these data rows will optimally improve model performance. See Prioritize high value data to label (active learning). |
Compare, model prediction, ground truth across model version
Understand the distribution of your training and inference data, splits, annotations, and predictions