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79 lines
2.7 KiB
Markdown
79 lines
2.7 KiB
Markdown
import Tabs from '@theme/Tabs';
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import TabItem from '@theme/TabItem';
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# MLModel & MLModelGroup
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## Why Would You Use MLModel and MLModelGroup?
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MLModel and MLModelGroup entities are used to represent machine learning models and their associated groups within a metadata ecosystem. They allow users to define, manage, and monitor machine learning models, including their versions, configurations, and performance metrics.
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### Goal Of This Guide
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This guide will show you how to
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- Create an MLModel or MLModelGroup.
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- Associate an MLModel with an MLModelGroup.
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- Read MLModel and MLModelGroup entities.
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## Prerequisites
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For this tutorial, you need to deploy DataHub Quickstart and ingest sample data.
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For detailed steps, please refer to [Datahub Quickstart Guide](/docs/quickstart.md).
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## Create MLModelGroup
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You can create an MLModelGroup by providing the necessary attributes such as name, platform, and other metadata.
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```python
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{{ inline /metadata-ingestion/examples/library/create_mlmodel_group.py show_path_as_comment }}
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```
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## Create MLModel
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You can create an MLModel by providing the necessary attributes such as name, platform, and other metadata.
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```python
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{{ inline /metadata-ingestion/examples/library/create_mlmodel.py show_path_as_comment }}
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```
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Note that you can associate an MLModel with an MLModelGroup by providing the group URN when creating the MLModel.
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You can also set MLModelGroup later by updating the MLModel entity as shown below.
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```python
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{{ inline /metadata-ingestion/examples/library/add_mlgroup_to_mlmodel.py show_path_as_comment }}
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```
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## Read MLModelGroup
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You can read an MLModelGroup by providing the group URN.
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```python
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{{ inline /metadata-ingestion/examples/library/read_mlmodel_group.py show_path_as_comment }}
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```
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#### Expected Output
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```python
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>> Model Group Name: My Recommendations Model Group
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>> Model Group Description: A group for recommendations models
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>> Model Group Custom Properties: {'owner': 'John Doe', 'team': 'recommendations', 'domain': 'marketing'}
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```
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## Read MLModel
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You can read an MLModel by providing the model URN.
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```python
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{{ inline /metadata-ingestion/examples/library/read_mlmodel.py show_path_as_comment }}
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```
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#### Expected Output
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```python
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>> Model Name: My Recommendations Model
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>> Model Description: A model for recommending products to users
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>> Model Group: urn:li:mlModelGroup:(urn:li:dataPlatform:mlflow,my-recommendations-model,PROD)
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>> Model Hyper Parameters: [MLHyperParamClass({'name': 'learning_rate', 'description': None, 'value': '0.01', 'createdAt': None}), MLHyperParamClass({'name': 'num_epochs', 'description': None, 'value': '100', 'createdAt': None}), MLHyperParamClass({'name': 'batch_size', 'description': None, 'value': '32', 'createdAt': None})]
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```
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