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md-docs/stylesheets/extra.css

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background-color: rgb(43, 155, 70);
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mask-image: var(--md-admonition-icon--code-block);
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md-docs/user_guide/data.md

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The [Data Schema] created for the [Task] contains a list of Column objects, each of which has a _Role_.
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Naturally, there is a relationship between the Column's Role and the Data Category.
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In fact, each Data Category comprises a set of Column objects with certain Roles.
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So that, when you upload samples belonging to a Data Category, they must contains all the Columns objects declared on the Data Schema to be considered valid.
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When you upload samples belonging to a Data Category, they must contain all the Columns objects declared on the Data Schema to be considered valid.
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The following table shows these relationships:
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You can set reference data as follow:
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``` py
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job_id = job_id = client.set_model_reference(
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job_id = client.set_model_reference(
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model_id=model_id,
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from_timestamp=from_timestamp,
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to_timestamp=to_timestamp,

md-docs/user_guide/detection_event_rules.md

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md-docs/user_guide/index.md

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- `type`: regression, classification, object detection ...
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- `data structure`: tabular data, image data, ...
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- `optional target`: if the target is not always available. This happen when input samples are labeled and the most part of production data do not have a label
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- `optional target`: if the target is not always available. This happens when input samples are labeled and the most part of production data do not have a label
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- `data schema`: specifies the inputs and the target of the task, see [Data Schema](data_schema.md) section for more details
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- `cost info`: information about the economic costs of the error on the target
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Discover how to setup automation rules to increase your reactivity.
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[:octicons-arrow-right-24: More info](detection_event_rules.md)
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[:octicons-arrow-right-24: More info](monitoring/detection_event_rules.md)
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- :material-lock:{ .lg .middle } **Roles and access**
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