Operationalization refers to the process of deploying R and Python models and code to Machine Learning Server in the form of 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.
Data scientists work locally with 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 R package and/or the 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.