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 launches Integrated Deployment: Groundbreaking approach removes the gap between creating data science and using it in production.

Anomaly Detection

Predict when critical equipment parts will go bad to prevent failures and downtime.

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.

Anomaly Detection

Inventory Level Optimization

Disease Tagging

Recommendation Engine for Retailers

Customer Sentiment Measurement

Risk Information Extraction

Extended Summit: What's coming up?

  • April 6: [L1-DS] KNIME Analytics Platform for Data Scientists: Basics.
  • April 7: [L2-DS] KNIME Analytics Platform for Data Scientists: Advanced
  • April 6 - 14: [L4-TS] Introduction to Time Series Analysis
  • April 8: Bringing Data Manipulation from KNIME into TIBCO Spotfire, by Maxime Guitet, Lionel Colliandre (Discngine)
  • April 9: KNIME Text Mining with NER Modeling and Deep Learning, by Julian Bunzel & Andisa Dewi (KNIME)
View program and register
Media article

Author: Paolo Tamagnini (KNIME)

Guided Labeling - 1 An Introduction to Active Learning

One of the key challenges of utilizing supervised machine learning for real world use cases is that most algorithms and models require lots of data with quite a few specific requirements. 

‐ 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. 

KNIME Users presenting their Use Cases

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