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Merge pull request #2311 from MicrosoftDocs/repo_sync_working_branch
Confirm merge from repo_sync_working_branch to main to sync with https://github.com/MicrosoftDocs/azure-ai-docs (branch main)
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articles/ai-services/translator/translator-faq.md

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@@ -60,7 +60,7 @@ If attribution is present, it must conform to the [Translator attribution guidel
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No, both have their place as essential tools for communication. Use machine translation where the quantity of content, speed of creation, and budget constraints make it impossible to use human translation.
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Machine translation os used as a first pass, before using human translation, by several of our [language service provider (LSP)](https://www.microsoft.com/translator/business/partners/) partners and can improve productivity by up to 50 percent. For a list of LSP partners, visit the Translator partner page.
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Machine translation is used as a first pass, before using human translation, by several of our [language service provider (LSP)](https://www.microsoft.com/translator/business/partners/) partners and can improve productivity by up to 50 percent. For a list of LSP partners, visit the Translator partner page.
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> [!TIP]

articles/machine-learning/how-to-monitor-model-performance.md

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After enabling feature importance, you'll see a feature importance for each feature you're monitoring in the Azure Machine Learning model monitoring studio UI.
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You can enable/disable alerts for each signal by setting `alert_enabled` property while using SDK or CLI.
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You can use Azure CLI, the Python SDK, or the studio for advanced setup of model monitoring.
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# [Azure CLI](#tab/azure-cli)
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advanced_data_drift = DataDriftSignal(
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reference_data=reference_data_training,
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features=features,
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metric_thresholds=metric_thresholds
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metric_thresholds=metric_thresholds,
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alert_enabled=True
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)
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# create an advanced prediction drift signal
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advanced_prediction_drift = PredictionDriftSignal(
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reference_data=reference_data_training,
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metric_thresholds=metric_thresholds
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metric_thresholds=metric_thresholds,
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alert_enabled=True
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)
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# create an advanced data quality signal
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reference_data=reference_data_training,
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features=features,
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metric_thresholds=metric_thresholds,
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alert_enabled=False
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alert_enabled=True
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# create feature attribution drift signal
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feature_attribution_drift = FeatureAttributionDriftSignal(
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reference_data=reference_data_training,
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metric_thresholds=metric_thresholds,
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alert_enabled=False
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alert_enabled=True
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# put all monitoring signals in a dictionary
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model_performance = ModelPerformanceSignal(
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production_data=production_data,
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reference_data=reference_data_ground_truth,
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metric_thresholds=metric_thresholds
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metric_thresholds=metric_thresholds,
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alert_enabled=True
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# put all monitoring signals in a dictionary
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production_data=production_data,
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reference_data=reference_data_training,
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features=features,
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metric_thresholds=metric_thresholds
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metric_thresholds=metric_thresholds,
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alert_enabled=True
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features=features,
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metric_thresholds=metric_thresholds,
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alert_enabled="False"
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alert_enabled=True
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# put all monitoring signals in a dictionary

articles/machine-learning/reference-migrate-sdk-v1-mlflow-tracking.md

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```
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> [!NOTE]
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> The `metrics` will only have the most recently logged value for a given metric. For example, if you log in order a value of `1`, then `2`, `3`, and finally `4` to a metric named `sample_metric`, only `4` will be present in the `metrics` dictionary. To get all metrics logged for a specific named metric, use [MlFlowClient.get_metric_history](https://mlflow.org/docs/latest/python_api/mlflow.tracking.html#mlflow.tracking.MlflowClient.get_metric_history):
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> The `metrics` will only have the most recently logged value for a given metric. For example, if you log in order a value of `1`, then `2`, `3`, and finally `4` to a metric named `sample_metric`, only `4` will be present in the `metrics` dictionary. To get all metrics logged for a specific named metric, use [MlFlowClient.get_metric_history](https://mlflow.org/docs/latest/python_api/mlflow.client.html?highlight=get_metric_history#mlflow.client.MlflowClient.get_metric_history):
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>
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> ```python
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> with mlflow.start_run() as multiple_metrics_run:
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> print(client.get_metric_history(multiple_metrics_run.info.run_id, "sample_metric"))
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> ```
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> For more information, see the [MlFlowClient](https://mlflow.org/docs/latest/python_api/mlflow.tracking.html#mlflow.tracking.MlflowClient) reference.
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> For more information, see the [MlFlowClient](https://mlflow.org/docs/latest/python_api/mlflow.client.html?highlight=get_metric_history#module-mlflow.client) reference.
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The `info` field provides general information about the run, such as start time, run ID, experiment ID, etc.:
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## View run artifacts
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To view the artifacts of a run, use [MlFlowClient.list_artifacts](https://mlflow.org/docs/latest/python_api/mlflow.tracking.html#mlflow.tracking.MlflowClient.list_artifacts):
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To view the artifacts of a run, use [MlFlowClient.list_artifacts](https://mlflow.org/docs/latest/python_api/mlflow.artifacts.html#mlflow.artifacts.list_artifacts):
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```python
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client.list_artifacts(finished_mlflow_run.info.run_id)
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To download an artifact, use [mlflow.artifacts.download_artifacts](https://www.mlflow.org/docs/latest/python_api/mlflow.tracking.html#mlflow.tracking.MlflowClient.download_artifacts):
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To download an artifact, use [mlflow.artifacts.download_artifacts](https://mlflow.org/docs/latest/python_api/mlflow.artifacts.html#mlflow.artifacts.download_artifacts):
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```python
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mlflow.artifacts.download_artifacts(run_id=finished_mlflow_run.info.run_id, artifact_path="Azure.png")

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