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azure-sql/includes/virtual-machines-best-practices-storage.md

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---
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author: MashaMSFT
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ms.author: mathoma
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ms.date: 03/20/2025
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ms.date: 12/09/2025
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ms.service: virtual-machines
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ms.topic: include
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---
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- Monitor the application and [determine storage bandwidth and latency requirements](/azure/virtual-machines/premium-storage-performance#counters-to-measure-application-performance-requirements) for SQL Server data, log, and `tempdb` files before choosing the disk type.
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- If available, configure the `tempdb` data and log files on the D: local SSD volume when you deploy a [new virtual machine](../virtual-machines/windows/storage-configuration.md#new-vms), or after you've [installed SQL Server manually](../virtual-machines/windows/tempdb-ephemeral-storage.md). The SQL IaaS Agent extension handles the folder and permissions needed upon re-provisioning.
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- To optimize storage performance, plan for highest uncached IOPS available and use data caching as a performance feature for data reads while avoiding [virtual machine and disks capping](/azure/virtual-machines/premium-storage-performance#throttling).
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- Set [host caching](/azure/virtual-machines/disks-performance#virtual-machine-uncached-vs-cached-limits) to **read-only** for data file disks.
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- Set [host caching](/azure/virtual-machines/disks-performance#virtual-machine-uncached-vs-cached-limits) to **none** for log file disks.
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- Don't enable read/write caching on disks that contain SQL Server data or log files.
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- Always stop the SQL Server service before changing the cache settings of your disk.
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- When using the [Ebdsv5 or Ebsv5](/azure/virtual-machines/ebdsv5-ebsv5-series) series SQL Server VMs, use [Premium SSD v2](../virtual-machines/windows/storage-configuration-premium-ssd-v2.md) for the best price performance. You can deploy your SQL Server VM with Premium SSD v2 by using the Azure portal (currently in preview).
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- If your workload requires more than 160,000 IOPS, use [Premium SSD v2](../virtual-machines/windows/performance-guidelines-best-practices-storage.md#premium-ssd-v2) or [Azure Ultra Disks](../virtual-machines/windows/performance-guidelines-best-practices-storage.md#azure-ultra-disk).
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- Place data, log, and `tempdb` files on separate drives.
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- For failover cluster instances (FCI) place `tempdb` on the shared storage.
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- If the FCI workload is heavily dependent on `tempdb` disk performance, then as an advanced configuration place `tempdb` on the local ephemeral SSD (default `D:\`) drive, which isn't part of FCI storage. This configuration needs custom monitoring and action to ensure the local ephemeral SSD (default `D:\`) drive is available all the time as any failures of this drive won't trigger action from FCI.
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- Stripe multiple Azure data disks using [Storage Spaces](/windows-server/storage/storage-spaces/overview) to increase I/O bandwidth up to the target virtual machine's IOPS and throughput limits.
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- Set [host caching](/azure/virtual-machines/disks-performance#virtual-machine-uncached-vs-cached-limits) to **read-only** for data file disks.
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- Set [host caching](/azure/virtual-machines/disks-performance#virtual-machine-uncached-vs-cached-limits) to **none** for log file disks.
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- Don't enable read/write caching on disks that contain SQL Server data or log files.
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- Always stop the SQL Server service before changing the cache settings of your disk.
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- When migrating several different workloads to the cloud, [Azure Elastic SAN](../virtual-machines/windows/storage-configuration-azure-elastic-san.md) can be a cost-effective consolidated storage solution. However, when using Azure Elastic SAN, achieving desired IOPS/throughput for SQL Server workloads often requires overprovisioning capacity. While not typically appropriate for single SQL Server workloads, you can attain a cost-effective solution when combining low-performance workloads with SQL Server.
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- For development and test workloads, and long-term backup archival consider using standard storage. It isn't recommended to use Standard HDD/SSD for production workloads.
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- [Credit-based Disk Bursting](/azure/virtual-machines/disk-bursting#credit-based-bursting) (P1-P20) should only be considered for smaller dev/test workloads and departmental systems.
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- To optimize storage performance, plan for highest uncached IOPS available, and use data caching as a performance feature for data reads while avoiding [virtual machine and disks capping/throttling](/azure/virtual-machines/premium-storage-performance#throttling).
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- Format your data disk to use 64-KB allocation unit size for all data files placed on a drive other than the temporary `D:\` drive (which has a default of 4 KB). SQL Server VMs deployed through Azure Marketplace come with data disks formatted with allocation unit size and interleave for the storage pool set to 64 KB.
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- Configure the storage account in the same region as the SQL Server VM.
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- Disable Azure geo-redundant storage (geo-replication) and use LRS (local redundant storage) on the storage account.
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---
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author: rwestMSFT
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ms.author: randolphwest
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ms.date: 12/08/2025
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ms.service: sql
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ms.topic: include
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---
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[!INCLUDE [Applies to](../../includes/applies-md.md)] [!INCLUDE [SQL Server 2025](_ss2025.md)] [!INCLUDE [Azure SQL Database](../../includes/applies-to-version/_asdb.md)] [!INCLUDE [_asmi](_asmi.md)] [!INCLUDE [Synapse Analytics](_asa.md)] [!INCLUDE [_fabric-dw](_fabric-dw.md)] [!INCLUDE [fabric-sqldb](_fabric-sqldb.md)]

docs/includes/fabric-sqldb.md

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ms.date: 12/08/2025
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SQL database in [!INCLUDE [fabric](fabric.md)]
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SQL database in Microsoft Fabric
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---
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title: "Deploy & manage Machine Learning Server web services in Python"
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title: "Deploy and Manage Machine Learning Server Web Services in Python"
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description: "This class is for SQL Machine Learning Services and Machine Learning Server for managing web services."
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author: VanMSFT
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ms.author: vanto
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ms.date: 2/16/2018
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ms.reviewer: randolphwest
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ms.date: 12/08/2025
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ms.service: sql
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ms.subservice: "machine-learning-services"
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ms.topic: "reference"
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monikerRange: ">=sql-server-2017||>=sql-server-linux-ver15"
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monikerRange: ">=sql-server-2017 || >=sql-server-linux-ver15"
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---
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# Machine Learning Server: manage web services with azureml-model-management-sdk
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**Applies to: Machine Learning Server, SQL Server 2017**
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# Manage web services with azureml-model-management-sdk (Machine Learning Server)
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'azureml-model-management-sdk' is a custom Python package developed by Microsoft. This package provides the classes and functions to deploy and interact with analytic web services. These web services are backed by code block and scripts in Python or R.
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[!INCLUDE [sssql17-md](../../../../includes/sssql17-md.md)]
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This topic is a high-level description of package functionality. These classes and functions can be called directly. For syntax and other details, see the individual function help topics in the table of contents.
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[!INCLUDE [machine-learning-server-retirement](../../../../includes/machine-learning-server-retirement.md)]
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<br/>
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`azureml-model-management-sdk` is a custom Python package developed by Microsoft. This package provides the classes and functions to deploy and interact with analytic web services. These web services are backed by code block and scripts in Python or R.
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This article is a high-level description of package functionality. These classes and functions can be called directly. For syntax and other details, see the individual function help topics in the table of contents.
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| Package details | Information |
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|--------|-|
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| Current version: | 1.0.1b7 |
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| --- | --- |
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| Current version: | 1.0.1b7 |
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| Built on: | [Anaconda](https://www.anaconda.com/) distribution of [Python 3.5](https://www.python.org/doc) |
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| Package distribution: | [Machine Learning Server 9.x](/machine-learning-server/what-is-machine-learning-server) </br>[SQL Server 2017 Machine Learning Server (Standalone)](../../../r/r-server-standalone.md) |
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| Package distribution: | [Machine Learning Server 9.x](/machine-learning-server/what-is-machine-learning-server)<br />[SQL Server 2017 Machine Learning Server (Standalone)](../../../r/r-server-standalone.md) |
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## How to use this package
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The **azureml-model-management-sdk** package is installed as part of Machine Learning Server and SQL Server 2017 Machine Learning Server (Standalone) when you add Python to your installation. It is also [available locally on Windows](/machine-learning-server/install/python-libraries-interpreter). When you install these products, you get the full collection of proprietary packages plus a Python distribution with its modules and interpreters.
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The `azureml-model-management-sdk` package is installed as part of Machine Learning Server and SQL Server 2017 Machine Learning Server (Standalone) when you add Python to your installation. It's also [available locally on Windows](/machine-learning-server/install/python-libraries-interpreter). When you install these products, you get the full collection of proprietary packages plus a Python distribution with its modules and interpreters.
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You can use any Python IDE to write Python scripts that call the classes and functions in **azureml-model-management-sdk**. However, the script must run on a computer having Machine Learning Server or SQL Server 2017 Machine Learning Server (Standalone) with Python.
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You can use any Python IDE to write Python scripts that call the classes and functions in `azureml-model-management-sdk`. However, the script must run on a computer having Machine Learning Server or SQL Server 2017 Machine Learning Server (Standalone) with Python.
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## Use cases
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There are three primary use cases for this release:
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There are three primary use cases for this release:
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+ Adding authentication logic to your Python script
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+ Deploying standard or real-time Python web services
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+ Managing and consuming these web services
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- Adding authentication logic to your Python script
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- Deploying standard or real-time Python web services
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- Managing and consuming these web services
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## Main classes and functions
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* [DeployClient](deploy-client.md)
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* [MLServer](mlserver.md)
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* [Operationalization](operationalization.md)
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* [OperationalizationDefinition](operationalization-definition.md)
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* [ServiceDefinition](service-definition.md)
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* [RealtimeDefinition](realtime-definition.md)
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* [Service](service.md)
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* [ServiceResponse](service-response.md)
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* [Batch](batch.md)
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* [BatchResponse](batch-response.md)
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## Next steps
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Add both Python modules to your computer by running setup:
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+ Set up [Machine Learning Server](/machine-learning-server/install/machine-learning-server-install) for Python or [Python Machine Learning Services](../../../install/sql-machine-learning-services-windows-install.md).
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Next, follow this quickstart to try it yourself:
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+ [Quickstart: How to deploy Python model as a service](/machine-learning-server/operationalize/python/quickstart-deploy-python-web-service)
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Or, read this how-to article:
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+ [How to publish and manage web services in Python](/machine-learning-server/operationalize/python/how-to-deploy-manage-web-services)
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## See also
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+ [Library Reference](/machine-learning-server/python-reference/introducing-python-package-reference)
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+ [Install Machine Learning Server](/machine-learning-server/what-is-machine-learning-server)
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+ [Install the Python interpreter and libraries on Windows](/machine-learning-server/install/python-libraries-interpreter)
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+ [How to authenticate in Python with this package](/machine-learning-server/operationalize/python/how-to-authenticate-in-python)
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+ [How to list, get, and consume services in Python with this package](/machine-learning-server/operationalize/python/how-to-consume-web-services)
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- [Class DeployClient](deploy-client.md)
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- [Class MLServer](mlserver.md)
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- [Class Operationalization](operationalization.md)
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- [Class OperationalizationDefinition](operationalization-definition.md)
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- [ServiceDefinition](service-definition.md)
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- [Class RealtimeDefinition](realtime-definition.md)
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- [Class Service](service.md)
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- [Class ServiceResponse](service-response.md)
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- [Class Batch](batch.md)
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- [Class BatchResponse](batch-response.md)
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## Related content
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- [Library Reference](/machine-learning-server/python-reference/introducing-python-package-reference)
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- [Install Machine Learning Server](/machine-learning-server/what-is-machine-learning-server)
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- [Install the Python interpreter and libraries on Windows](/machine-learning-server/install/python-libraries-interpreter)
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- [How to authenticate in Python with this package](/machine-learning-server/operationalize/python/how-to-authenticate-in-python)
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- [How to list, get, and consume services in Python with this package](/machine-learning-server/operationalize/python/how-to-consume-web-services)
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- [Machine Learning Server](/machine-learning-server/install/machine-learning-server-install)
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- [Install SQL Server Machine Learning Services (Python and R) on Windows](../../../install/sql-machine-learning-services-windows-install.md)
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- [Quickstart: How to deploy Python model as a service](/machine-learning-server/operationalize/python/quickstart-deploy-python-web-service)
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- [How to publish and manage web services in Python](/machine-learning-server/operationalize/python/how-to-deploy-manage-web-services)

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