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Segment specific reference
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‎docs/sitemap.xml‎

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<urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9">
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<loc>https://ml-cube.github.io/ml3-platform-docs/</loc>
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<loc>https://ml-cube.github.io/ml3-platform-docs/api/python/</loc>
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</url>
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</url>
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<loc>https://ml-cube.github.io/ml3-platform-docs/user_guide/rbac/</loc>
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</url>
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</url>
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<loc>https://ml-cube.github.io/ml3-platform-docs/user_guide/modules/topic_modeling/</loc>
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</url>
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</urlset>

‎md-docs/user_guide/data.md‎

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The [Monitoring] module requires the definition of a Reference dataset representing training, validation and test data.
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As mentioned before, historical data belong to both training and old data, but it is important distinguish them.
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Therefore, the reference data is initially loaded as historical data and then marked as reference data by providing the timestamp interval.
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Therefore, the reference data is initially loaded as historical data and then marked as reference data by providing the timestamp interval. This interval is defined as one or more [from timestamp, to timestamp] tuples. It serves also as default interval being applied to any [Segment] without a specific time range. Optionally, segment-specific intervals can be provided, also as lists of [from timestamp, to timestamp] tuples.
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To provide sufficient statistical reliability, reference data must include at least 300 samples. This requirement also applies to the reference of each [Segment], if the task involves any.
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??? code-block "SDK Example"
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You can set reference data as follow:
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You can set the default reference data as follow:
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``` py
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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,
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default_reference=ReferenceInfo(
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time_intervals=[(from_timestamp, to_timestamp)]
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),
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)
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```
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In case you want to set multiple default intervals:
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``` py
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job_id = client.set_model_reference(
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model_id=model_id,
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default_reference=ReferenceInfo(
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time_intervals=[(from_timestamp_1, to_timestamp_1), (from_timestamp_2, to_timestamp_2)]
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),
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)
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```
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If you want to set also segment-specific reference intervals:
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``` py
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job_id = client.set_model_reference(
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model_id=model_id,
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default_reference=ReferenceInfo(
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time_intervals=[(from_timestamp, to_timestamp)]
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),
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segment_references=[ReferenceInfo(time_intervals=[(from_timestamp_segment_1, to_timestamp_segment_1)], segment_id=segment_id_1),
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ReferenceInfo(time_intervals=[(from_timestamp_segment_2, to_timestamp_segment_2)], segment_id=segment_id_2)]
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)
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```
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Segment intervals can also include multiple (from, to) tuples.
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### Production
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After the artificial intelligence model is deployed in a production environment, incoming data belongs to the Production context.

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