Machine Learning Server Operationalization One-box Windows

By for March 26, 2018

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This solution installs Microsoft Machine Learning Server on Windows VM and configures as One-box to act as a deployment server and to host analytic web services for operationalization
> **Note:** If you have already deployed this solution, click [here](https://quickstart.azure.ai/track/Deployments?type=o16noneboxwindows) to view your deployment. ### Estimated Provisioning Time: 15 Minutes ## Overview Operationalization refers to the process of deploying R and Python models and code to Machine Learning Server in the form of [web services](https://docs.microsoft.com/en-us/machine-learning-server/operationalize/concept-what-are-web-services) and the subsequent consumption of these services within client applications to affect business results. Today, more businesses are adopting advanced analytics for mission critical decision making. Typically, data scientists first build the predictive models, and only then can businesses deploy those models in a production environment and consume them for predictive actions. Being able to operationalize your analytics is a central capability in Machine Learning Server. After installing Machine Learning Server on select platforms, you'll have everything you need to [configure the server to securely host R and Python analytics web services](https://docs.microsoft.com/en-us/machine-learning-server/operationalize/configure-start-for-administrators#configure-server-for-operationalization). For details on which platforms, see [Supported platforms](https://docs.microsoft.com/en-us/machine-learning-server/operationalize/configure-start-for-administrators#supported-platforms). Data scientists work locally with [Microsoft R Client](https://docs.microsoft.com/en-us/machine-learning-server/r-client/what-is-microsoft-r-client), with Machine Learning Server, or with any other program in their preferred IDE and favorite version control tools to build scripts and models using open-source algorithms and functions and/or our proprietary ones. Using the [mrsdeploy](https://docs.microsoft.com/en-us/machine-learning-server/r-reference/mrsdeploy/mrsdeploy-package) R package and/or the [azureml-model-management-sdk](https://docs.microsoft.com/en-us/machine-learning-server/python-reference/azureml-model-management-sdk/azureml-model-management-sdk) Python package that ships the products, the data scientist can develop, test, and ultimately deploy these R and Python analytics as web services in their production environment. Once deployed, the analytic web service is available to a broader audience within the organization who can then, in turn, consume the analytics. Machine Learning Server provides the operationalizing tools to deploy R and Python analytics inside web, desktop, mobile, and dashboard applications and backend systems. Machine Learning Server turns your scripts into analytics web services, so R and Python code can be easily executed by applications running on a secure server. <img src="https://ciqsdatastorage.blob.core.windows.net/o16noneboxwindows/data-scientist-easy-deploy.png" > ## One-box Configuration Architecture A one-box configuration, as the name suggests, involves a single [web node and compute node](https://docs.microsoft.com/en-us/machine-learning-server/operationalize/configure-start-for-administrators#configure-server-for-operationalization) run on a single machine along with a database. This configuration is useful when you want to explore what it is to operationalize R and Python analytics using Machine Learning Server. It is perfect for testing, proof-of-concepts, and small-scale prototyping. <img src="https://ciqsdatastorage.blob.core.windows.net/o16noneboxwindows/setup-onebox.png" > ## Pricing Your Azure subscription used for the deployment will incur consumption charges on the VM used in this solution. >Please ensure that you stop your VM instance when not actively using the solution. Running the VM will incur higher costs. > >**Please delete the solution if you are not using it.** ## Disclaimer ©2017 Microsoft Corporation. All rights reserved. This information is provided "as-is" and may change without notice. Microsoft makes no warranties, express or implied, with respect to the information provided here. Third party data was used to generate the Solution. You are responsible for respecting the rights of others, including procuring and complying with relevant licenses in order to create similar datasets. ![ ](https://quickstart.azure.ai/track?solutionid=o16noneboxwindows)