The goal of this article, by Rosaria Silipo (KNIME), is to help clarify the difference between metanodes and components. What's a metanode? What's a component? And when do you use what?
Welcome to the sixth episode of Guided Labeling KNIME Blog Series by Paolo Tamagning and Adrian Nembach (KNIME).
In the last episode we made an analogy with a number of “friends” labeling “movies” with three different outcomes:“good movie” (👍), “not seen movie” ( - ), “bad movie” (👎). We have seen how we can train a machine learning model predicting also movies no friend has watched before and adding to the model additional feature data about such movies. Let’s pick up where we left off.
In this blog series we’ll be experimenting with the most interesting blends of data and tools. Whether it’s mixing traditional sources with modern data lakes, open-source devops on the cloud with protected internal legacy tools, SQL with noSQL, web-wisdom-of-the-crowd with in-house handwritten notes, or IoT sensor data with idle chatting, we’re curious to find out: will they blend? Want to find out what happens when IBM Watson meets Google News, Hadoop Hive meets Excel, R meets Python, or MS Word meets MongoDB?
Follow us here and send us your ideas for the next data blending challenge you’d like to see at firstname.lastname@example.org.
In today’s challenge we’re going to blend data on a SAP system that is accessed in two ways.
In this second episode of the Integrated Deployment Blog Series - a series of articles focusing on solving the challenges around productionizing data science - we look at the Model part of the process.
Welcome to the Integrated Deployment Blog Series, a series of articles focusing on solving the challenges around productionizing data science.
Welcome to the fifth episode of our Guided Labeling KNIME Blog Series. In the last four episodes, we introduced Active Learning and a practical example with body mass index data, which shows how to perform active learning sampling via the technique “exploration vs exploitation”. This technique employs label density and model uncertainty to select which rows should be labeled first by the user of our active learning application deployed on KNIME WebPortal.
Today, all of our digital devices and sensors are interconnected in the Internet of Things. tarent has built an extension for KNIME Analytics Platform that enables you to connect to Software AG’s Cumulocity Iot platform so that you can use the more advanced analytics provided by KNIME on your Cumulocity data.
How the core concepts of time series fit the process of accessing, cleaning, modeling, forecasting, and reconstructing time series
High throughput biochemical and phenotypic screening (HTS) enables scientists to test thousands of samples simultaneously. Using automation, the effects of thousands of compounds can be evaluated on cultured cells, or using biochemical in vitro assays. The goal of HTS is to be able to identify or “hit” compounds that match certain properties. As HTS is usually conducted on very large libraries of compounds the volume of raw data that is produced is usually huge. This calls for an analysis tool that is able to handle large volumes of data easily.
Most likely, the assumptions behind your data science model or the patterns in your data did not survive the coronavirus pandemic. Here’s how to address the challenges of model drift.