try h5

Author: v-thepet

articles/machine-learning/how-to-manage-models.md

@@ -229,70 +229,70 @@ If your model data comes from a job output, you have two options for specifying
 >[!NOTE]
 >The *artifacts* reserved keyword represents output from the default artifact location.
 
-- **MLflow runs: URI format**
+##### MLflow runs: URI format
 
-  This option is optimized for MLflow users, who are probably already familiar with the MLflow `runs:` URI format. This option creates a model from artifacts in the default artifact location, where all MLflow-logged models and artifacts are located. This option also establishes a lineage between a registered model and the run the model came from.
+This option is optimized for MLflow users, who are probably already familiar with the MLflow `runs:` URI format. This option creates a model from artifacts in the default artifact location, where all MLflow-logged models and artifacts are located. This option also establishes a lineage between a registered model and the run the model came from.
 
-  Format: `runs:/<run-id>/<path-to-model-relative-to-the-root-of-the-artifact-location>`
+Format: `runs:/<run-id>/<path-to-model-relative-to-the-root-of-the-artifact-location>`
 
-  Example:
+Example:
 
-  # [Azure CLI](#tab/cli)
+# [Azure CLI](#tab/cli)
 
-  ```azurecli
-  az ml model create --name my-registered-model --version 1 --path runs:/my_run_0000000000/model/ --type mlflow_model
-  ```
+```azurecli
+az ml model create --name my-registered-model --version 1 --path runs:/my_run_0000000000/model/ --type mlflow_model
+```
+
+# [Python SDK](#tab/python)
 
-  # [Python SDK](#tab/python)
+```python
+from azure.ai.ml.entities import Model
+from azure.ai.ml.constants import ModelType
 
-  ```python
-  from azure.ai.ml.entities import Model
-  from azure.ai.ml.constants import ModelType
-  
-  run_model = Model(
-      path="runs:/my_run_0000000000/model/"
-      name="my-registered-model",
-      description="Model created from run.",
-      type=ModelType.MLFLOW_MODEL
-  )
-  
-  ml_client.models.create_or_update(run_model) 
-  ```
+run_model = Model(
+    path="runs:/my_run_0000000000/model/"
+    name="my-registered-model",
+    description="Model created from run.",
+    type=ModelType.MLFLOW_MODEL
+)
+
+ml_client.models.create_or_update(run_model) 
+```
 
 ---
 
-- **azureml://jobs URI format**
+##### azureml://jobs URI format
 
-  The `azureml://jobs` reference URI option lets you register a model from artifacts in any of the job's output paths. This format aligns with the `azureml://datastores` reference URI format, and also supports referencing artifacts from named outputs other than the default artifact location.
+The `azureml://jobs` reference URI option lets you register a model from artifacts in any of the job's output paths. This format aligns with the `azureml://datastores` reference URI format, and also supports referencing artifacts from named outputs other than the default artifact location.
 
-  If you didn't directly register your model within the training script by using MLflow, you can use this option to establish a lineage between a registered model and the job it was trained from.
+If you didn't directly register your model within the training script by using MLflow, you can use this option to establish a lineage between a registered model and the job it was trained from.
 
-  Format: `azureml://jobs/<run-id>/outputs/<output-name>/paths/<path-to-model>`
+Format: `azureml://jobs/<run-id>/outputs/<output-name>/paths/<path-to-model>`
 
-  - Default artifact location: `azureml://jobs/<run-id>/outputs/artifacts/paths/<path-to-model>/`. This location is equivalent to MLflow `runs:/<run-id>/<model>`.
-  - Named output folder: `azureml://jobs/<run-id>/outputs/<named-output-folder>`
-  - Specific file within the named output folder: `azureml://jobs/<run-id>/outputs/<named-output-folder>/paths/<model-filename>`
-  - Specific folder path within the named output folder: `azureml://jobs/<run-id>/outputs/<named-output-folder>/paths/<model-folder-name>`
+- Default artifact location: `azureml://jobs/<run-id>/outputs/artifacts/paths/<path-to-model>/`. This location is equivalent to MLflow `runs:/<run-id>/<model>`.
+- Named output folder: `azureml://jobs/<run-id>/outputs/<named-output-folder>`
+- Specific file within the named output folder: `azureml://jobs/<run-id>/outputs/<named-output-folder>/paths/<model-filename>`
+- Specific folder path within the named output folder: `azureml://jobs/<run-id>/outputs/<named-output-folder>/paths/<model-folder-name>`
 
-  Example:
-  
-  # [Azure CLI](#tab/cli)
+Example:
 
-  Save a model from a named output folder:
+# [Azure CLI](#tab/cli)
 
-  ```azurecli
-  az ml model create --name run-model-example --version 1 --path azureml://jobs/my_run_0000000000/outputs/artifacts/paths/model/
-  ```
+Save a model from a named output folder:
+
+```azurecli
+az ml model create --name run-model-example --version 1 --path azureml://jobs/my_run_0000000000/outputs/artifacts/paths/model/
+```
 
-  For a complete example, see the [CLI reference](/cli/azure/ml/model).
+For a complete example, see the [CLI reference](/cli/azure/ml/model).
 
-  # [Python SDK](#tab/python)
+# [Python SDK](#tab/python)
 
-  Save a model from a named output:
+Save a model from a named output:
 
-  [!notebook-python[] (~/azureml-examples-main/sdk/python/assets/model/model.ipynb?name=run_model)]
+[!notebook-python[] (~/azureml-examples-main/sdk/python/assets/model/model.ipynb?name=run_model)]
 
-  For a complete example, see the [model notebook](https://github.com/Azure/azureml-examples/blob/main/sdk/python/assets/model/model.ipynb).
+For a complete example, see the [model notebook](https://github.com/Azure/azureml-examples/blob/main/sdk/python/assets/model/model.ipynb).
 
 ---