Integrated deployment approach plugs the gap between data science and production

While data is essential to businesses it increasingly seems that there is a gap between creating data science and actually using the information in production.

Open source analytics company KNIME is aiming to eliminate this gap with the launch of Integrated Deployment.

Until now, the process of moving a data model into production and applying it to new customers has required manual replication of the exact data creation and model settings to ensure that the model could be usable in production. With KNIME Integrated Deployment, however, the created model as well as all required steps and settings are automatically captured and packaged so that the entire production process is, for the first time, instantly available for production use.

"Our open approach and close collaboration with the community means that KNIME is always at the forefront of what is possible in data science. Integrated Deployment represents another big step forward," says Michael Berthold, CEO and co-founder of KNIME. "This solves perhaps one of the biggest problems in data science today by completely eliminating the gap between the art of data science creation and moving the results into production."

It works using the open-source KNIME Analytics Platform where a workflow is created to generate an optimal model. Integrated Deployment then allows a data scientist to mark the portions of the workflow that would be necessary for running in a production environment, including data creation and preparation as well as the model itself, and save them automatically as workflows with all appropriate settings and transformations included. There is no limitation to this identification process and it can be simple or as complex as the project requires.

You can find out more on the KNIME website.

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