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.

I like your gut feeling better. Can I have your gut microbes?

Microbiomes live inside us and on us and are real multi-taskers. They break down nutrients that our body couldn’t break down by itself. They train our immune system. And they are first in line in our defense against pathogens. Our health depends on them.

One of the key challenges in using supervised machine learning for real world use cases is that most algorithms and models require a sample of data that is large enough to represent the actual reality your model needs to learn.

These data need to be labeled. These labels will be used as the target variable when your predictive model is trained. In this series we've been looking at different labeling techniques that improve the labeling process and save time and money.

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!

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.

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

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