Configure and train models
This page walks through how to configure, train, and tune machine learning models on your labeled dataset in Studio.
Model training progress stages
This guide explains the training progress stages you will encounter when you initiate model training in a Snorkel Flow Application. Monitoring the training prog...
Model training in the SDK
This guide details the process for training models outside the Snorkel Flow UI using the Python Software Development Kit (SDK). This method is particularly usef...
Continuous model validation
This article provides a comprehensive guide to continuous model validation, emphasizing its critical role in maintaining the accuracy and effectiveness of your ...
Supported modeling libraries
This page describes the three major modeling libraries that Snorkel Flow supports: Scikit-Learn, XGBoost, and HuggingFace’s Transformers. If you think that your...
View and analyze model results
This page walks through the model results and metrics that are available in the Models pane. The Analysis pane provides additional charts, metrics, and guidance...