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Also we added a utility method to retrieve from the user's accessible models, the last trained one. Last is determined based on timestamp when it was saved into the model registry.
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=== "Python"
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```python
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#Retrieve newest model from all user's accessible models based on this feature view
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model = fraud_fv.get_newest_model()
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#Retrieve newest model from all user's accessible models based on this training dataset version
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model = fraud_fv.get_newest_model(training_dataset_version: 1)
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```
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### Using the UI
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In the feature view overview UI you can explore the provenance graph of the feature view:
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The relationship between feature views and models is captured automatically when you create a model. You can inspect the relationship between feature views and models using the APIs or the UI.
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=== "Python"
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```python
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lineage = model.get_feature_view_provenance()
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# List all accessible parent feature views
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lineage.accessible
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# List all deleted parent feature views
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lineage.deleted
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# List all the inaccessible parent feature views
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lineage.inaccessible
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```
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You can also retrieve the training dataset provenance object.
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=== "Python"
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```python
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lineage = model.get_training_dataset_provenance()
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# List all accessible parent training datasets
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lineage.accessible
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# List all deleted parent training datasets
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lineage.deleted
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# List all the inaccessible parent training datasets
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lineage.inaccessible
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```
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You can also retrieve directly the parent feature view object, without the need to extract them from the provenance links object
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=== "Python"
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```python
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feature_view = model.get_feature_view()
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```
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This utility method also has the options to initialize the required components for batch or online retrieval of feature vectors.
By default, the base init for feature vector retrieval is enabled. In case you have a workflow that requires more particular options, you can disable this base init by setting the `init` to `false`.
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The method detects if it is running within a deployment and will initialize the feature vector retrieval for the serving.
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If the `online` argument is provided and `true` it will initialize for online feature vector retrieval.
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If the `online` argument is provided and `false` it will initialize the feature vector retrieval for batch scoring.
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