KNIME removes barriers between model development and production

KNIME removes barriers between model development and production

Max Smolaks

April 3, 2020

3 Min Read

The new Integrated Deployment functionality saves time and effort

by Max Smolaks 2 April 2020

the open source data analytics platform, has
added functionality that enables data scientists to move models from
development to production without having to alter any code.

Deployment identifies and
packages not
just the model, but all of
its associated data
preparation and
steps so they can be
automatically reused.

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,” said Michael
Berthold, co-founder and CEO of KNIME.

Integrated Deployment was launched at the KNIME Spring Summit 2020, taking place this year as an online-only event.



The development of KNIME Analytics Platform (from Konstanz Information Miner) is led by KNIME the company, headquartered in Zurich. It is used for a variety of purposes including data mining, business intelligence and machine learning.

KNIME (the platform) started out in 2006 as a proprietary software product, but made a pivot to GPLv3 – the most ‘hardcore’ free and open source license – with the release of version 2.1 in 2009. This means it can be downloaded, shared and modified without any restrictions, and there’s even a special provision that enables other companies to develop new ‘nodes’ for KNIME and sell them.

The Integrated Deployment process aims to simplify the lives of data scientists that build their models on KNIME. Previously, moving a model into production required manual replication of the exact data creation and model settings; now these can be maintained automatically.

Here’s how it works, according to the company: “Using
open-source KNIME Analytics Platform, a workflow is created to
generate an optimal model. Integrated Deployment 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 saved. There is no limitation in this identification
process — it can be simple or as advanced (and complex) as

Server in production, these captured workflows are then referenced
and reused. There is no need to rewrite or recode any of the

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