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# Onboarding

Onboarding prepares an ingested datasource for a prediction task. It applies the target, selected features, prediction type, preprocessing, sharding and encoding choices that make the data ready for training.

The output is a dataset. The datasource describes the imported data; the dataset represents that data after the onboarding choices have been applied.

## How onboarding works

#### Datasource

Onboarding starts from an available datasource created by ingestion.

#### Configuration

The onboarding configuration defines the prediction task and how to prepare the data, including missing-value handling and categorical preprocessing.

#### Job

The onboarding job applies the configuration to the selected datasource using processing compute.

#### Dataset

A successful job creates or updates a dataset. Inspect its fields, distributions and lineage before selecting it for training.

![Onboarding resources and their relationships](/_fern-files/_img/4d261d8ef2e787d0d3f1878c3ff7620188e17df0b40c406b82c27e3a6ab60c81.webp)

## Initial and incremental onboarding

An **Initial** job creates a dataset. An **Incremental Update** job updates an existing dataset from a datasource containing additional data. The configuration must match the job type; an optional **Increment label** identifies the update.

Updating a dataset does not change a model already trained from it. Run a new training job when you need a model built from the updated data.

## Find onboarding resources in the app

Under **Data Lab > Datasets**, use onboarding configurations for the preparation choices, onboarding jobs for execution, and datasets for the outputs. A configuration is reusable; each job records a particular run and its resource references.

## Next steps

Create an [onboarding configuration](/ui-guides/data/onboarding-configuration), run an [onboarding job](/ui-guides/data/onboarding-jobs), then [inspect the dataset](/ui-guides/data/datasets).