End to End Data Science

At KNIME, we build software to create and productionize data science using one easy and intuitive environment, enabling every stakeholder in the data science process to focus on what they do best.

How can KNIME Software help you?


Gather & Wrangle

Access, merge, and transform all of your data

Model & Visualize

Make sense of your data with the tools you choose


Deploy & Manage

Support enterprise-wide data science practices

Consume & Optimize

Leverage insights gained from your data
KNIME Analytics Platform

KNIME Analytics Platform

KNIME Analytics Platform is the free, open-source software for creating data science.

KNIME Server Guided Analytics via Web Portal

KNIME Server

KNIME Server is the commercial solution for productionizing data science.

Contributor of the Month

We are happy to announce Vijaykrishna Venkataraman's mind map of KNIME features as the community contribution in August. In his words, "these are just my picks and don’t cover the entire list."

Each month we highlight community members who are doing unique and interesting things with KNIME or sharing useful data science tips and tricks.

Thanks for sharing, Vijay!

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Automated Workflow Testing and Validation

Automate testing, save time, and catch errors early.

Inventory Level Optimization

Achieve the perfect trade-off between inventory costs and service level.

Disease Tagging

Reduce time spent sifting through medical literature with automatic disease tagging.

Recommendation Engine for Retailers

Increase store level sales through better brand portfolio decision making.

Customer Sentiment Measurement

Evaluate customer pain points to better allocate and manage resources.

Risk Information Extraction

Remove the need for manual work by automatically gathering and harmonizing text-based information.

Automated Workflow Testing and Validation

Inventory Level Optimization

Disease Tagging

Recommendation Engine for Retailers

Customer Sentiment Measurement

Risk Information Extraction

Media article

Welcome to the seventh episode of our series of Guided Labeling Blog Posts1 by Paolo Tamagnini and Adrian Nembach (KNIME). In the previous episodes we have covered active learning and weak supervision theory. Today, we would like to present a practical example based on a KNIME Workflow and implementing Weak Supervision via Guided Analytics.

Guided Labeling Model Uncertainty
‐ by Paolo Tamagnini
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KNIME Software: Creating and Productionizing Data Science

Be part of the KNIME Community

Join us, along with our global community of users, developers, partners and customers in sharing not only data science, but also domain knowledge, insights and ideas.