How to move data science into production
Deploying data science into production is still a big challenge. Not only does the deployed data science need to be updated frequently but available data sources and types change rapidly, as do the methods available for their analysis. This continuous growth of possibilities makes it very limiting to rely on carefully designed and agreed-upon standards or work solely within the framework of proprietary tools.
KNIME has always focused on delivering an open platform, integrating the latest data science developments by either adding our own extensions or providing wrappers around new data sources and tools. This allows data scientists to access and combine all available data repositories and apply their preferred tools, unlimited by a specific software supplier’s preferences. When using KNIME workflows for production, access to the same data sources and algorithms has always been available, of course. Just like many other tools, however, transitioning from data science creation to data science production involved some intermediate steps.