25 May 2020emilio_s

Everybody loves charts, graphs...visualizations! They are neat, fast, and straightforward. Even with messy and disorganized data, a good visualization is the key to show insights and features that are difficult to point out on a raw table. In this blog post I will show you how to build a simple, but useful and good-looking dashboard to present your data - in three simple steps!

Create an interactive dashboard in 3 steps

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18 May 2020andisa.dewi

Continuing with our series of articles about cloud connectivity, this blog post is an introduction of how to use KNIME on Databricks. It's written as a guide, showing you how to connect to a Databricks cluster within KNIME Analytics Platform, as well as looking at several ways to access data from Databricks and upload them back to Databricks.

KNIME on Databricks

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11 May 2020paolotamag

Welcome to the third episode of our series on Guided Labeling!

Guided Labeling Model Uncertainty

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07 May 2020berthold

By Michael Berthold (KNIME). As first published in InfoWorld.

With new Integrated Deployment extensions, data scientists can capture entire KNIME workflows for automatic deployment to production or reuse

How to move data science into production

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04 May 2020rs

I know you are still using Excel sheets to transform and/or analyze your data! I know, because most of us still use it to some extent. There is nothing wrong with using Excel. Excel spreadsheets are a great tool to collect and transform small amounts of data. However, when the game becomes harder and requires larger amounts of data, Excel starts showing its limitations.

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27 Apr 2020Jeany

Express Yourself!

All individuals are unique and so are our data needs. From simple csv files to REST APIs to Google’s BigQuery or using customized shared components, KNIME Analytics Platform offers many ways to access and analyze your data. Today, we will demonstrate how to access all of these aforementioned data sources through the use case of analyzing and annotating gene expression data.

Analyzing Gene Expression Data with KNIME

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20 Apr 2020Pharmacelera

by Enric Herrero (Pharmacelera)

What is virtual screening in pharmaceutical R&D?

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06 Apr 2020Martyna

Accessing scientific datasets in Google Bigquery

The availability of scientific datasets in Google BigQuery opens new possibilities for the exploration and analysis of public life sciences data. Especially the Google Cloud Platform (GCP) provides a place where SQL queries can be easily and intuitively created in order to explore huge datasets extremely fast.

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30 Mar 2020paolotamag

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. 

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