Customer Feedback for Microsoft R

R Server Operationalization

How can we improve Microsoft R Server Operationalization?

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  1. Model lifecycle Management

    Model version control; central repository for models; roll back to previous version; monitor model performance and drift in production; compare model accuracy; retire models when they are not accurate;

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    • Use Azure Batch as the R Server Operationalization Grid

      Azure batch is a great service for job scheduling and also for scaling the computing. It will be cool that it can be easily configured as the compute nodes of R Server O16N, so that we can set up a grid easily with less cost (comparing with R Server VMs), scale up and down automatically and can monitor the usage.

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      • A tool to centralized the management and configuration for R Server Operationalization Grid

        Today, to set up an R Server Operationalization Grid with multiple web nodes and compute nodes, I have to install and configure for each node. A GUI tool to centralize the set up and configuration will be very helpful. It will be cool if the tool can help monitor the resource usage (CPU/Memory) of the grid.

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        • GUI for Admin Utility Tool

          The current Admin Utility Tool is command line based, which is not easy to use in terms of navigation and content richness. A GUI application is preferred.

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          • Need a UI to manage and monitor the web services

            Currently R Server only supports command line interface to view the web services published into R Server. It is not ease-of-use. We would like to see a UI to manage and monitor the web services (list/search/delete/usage dashboard).

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            • R Server supports multiple R engines

              Currently each R Server version supports only one R engine. For example, R Server 8.0.5 supports only R 3.2.2 and R Server 9.1 only supports R3.3.3. As a data scientist, it is very common for me to try and build my R code with latest version of the packages I choose. Many of those packages work with only the latest R version. Therefore, it is very inconvenience for me using those packages with my team's R Server (which is attached with an older version R engine).

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              • Deploy models to Edge devices

                Current R Server operationalization is all about deploying models as web services. There are many cases, especially for IOT edge devices, that internet connection are either not stable or not affordable. We want to run advanced analytics in Edge devices (for example running in Raspberry pi).

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                • GUI Toolsets for IT Admins to manage package repos

                  R users know how to install packages locally. Some of them even know how to set up their own package repositories with open source technologies such as packrat, checkpoint, miniCRAN, draft, switchr. But as an enterprise, I would like to manage those open source packages by my IT admins to keep them compliant with our security requirements. We need GUI Toolsets for IT admins who are not familiar with R to manage the packages and repos.

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                  • Install packages to all operationalization nodes easily

                    As a data scientist using R, I like R Server's operationalization capabilities to deploy my models. There are some common used R packages in my team, such as dplyr, ggplot2 which are not in R Server's bundle. Today, I have to ask admin to install them into each nodes of the operationalization grid. My admin would like to grant me the right to do this. But even I have the right, there is no easy to install those packages to all the nodes easily.

                    2 votes
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