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Copy file name to clipboardExpand all lines: articles/ai-services/translator/translator-faq.md
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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.
Copy file name to clipboardExpand all lines: 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.
> 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):
> 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.:
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):
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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