dagshub-docs/docs/use_cases/data_engine/index.md

title	description
Manage Datasets with DagsHub Data Engine	Documentation on using Data Engine to create training-ready datasets that you can enrich, query, visualize, annotate and train with.

Curating and Managing Datasets

DagsHub helps you manage your large scale datasets easily, so you can focus on improving your models. To do this, we built Data Engine which includes tools and APIs for querying, visualizing, annotating, and generating dataloaders to easily train, evaluate and debug your models.

The following use cases will guide you on how to use the Data Engine end to end; from connecting and enriching your data to querying and visualizing, annotating and finally training and improving your model.

Before using DagsHub Data Engine, make sure you have the DagsHub Client installed:

pip install dagshub

next, make sure to import the Datasource class:

from dagshub.data_engine import datasources

Start using Data Engine by connecting your datasource

• :material-connection:{.lg .middle}  Connect Datasource

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  Connect the data you want to work with

• :material-expand-all:{.lg .middle}  Enrich Data

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  Add custom metadata, predictions and labels to your data

• :simple-googlebigquery:{.lg .middle}  Query Data

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  Query your data and generate new subsets to re-train your model

• :octicons-eye-16:{.lg .middle}  Visualize your data

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  Visualize data points and their enrichments

• :dagshub-annotations:{.lg .middle}  Annotate your data

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  Annotate relevant data points

• :octicons-ai-model-24:{.lg .middle}  Train a model

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  Train and improve your model with new datasets