Author: Corey Weisinger
You’ve always been able to fine tune and modify your networks in KNIME Analytics Platform by using the Deep Learning Python nodes such as the DL Python Network Editor or DL Python Learner, but with recent updates to KNIME Analytics Platform and the KNIME Deep Learning Keras Integration there are more tools available to do this without leaving the familiar KNIME GUI.
Author: Kenneth Longo
The cheminformatics world is replete with software tools and file formats for the design, manipulation and management of small molecules and libraries thereof. Those tools and formats are often specialized in analyzing small molecules of ~500 daltons, give or take a few, or those molecules that can reasonably be drawn and understood using classic ball-and-stick or molecular coordinate frameworks. Perhaps not coincidentally, this neatly envelops the needs of small molecule drug discovery, where it is not uncommon to find both public and privately-held repositories of hundreds of thousands (to millions) of such molecules, for use in molecular or phenotypic screening assays. The small size and elemental simplicity of these molecules has resulted in a variety of storage file formats (e.g., mol, SMILES, sdf, etc) and many supporting software packages (e.g., RDkit, CDK, ChemAxon, etc) for visualization and manipulation that support them. KNIME Analytics Platform provides easy access to those file formats and software packages.
Recently on social media we asked you for tips on tidying up and improving workflows. Our aim was to find out how you declutter to make your workflows not just superficially neater, but faster, more efficient, and smaller: ultimately an elegant masterpiece! Check out the original posts on LinkedIn and Twitter.
Author: James Weakley
We’d like to introduce James Weakley, a Data Architect at nib health funds, who recently wrote a short blog post on the topic of KNIME Server and Okta. James has given us permission to republish it here. But first a few words about James.
The data science dilemma: Automation, APIs, or custom data science?
As companies place an increasing premium on data science, there is some debate about which approach is best to adopt — and there is no straight up, one-size-fits-all answer. It really depends on your organization’s needs and what you hope to accomplish.
There are three main approaches that have been discussed over the past couple of years; it’s worth taking a look at the merits and limitations of each as well as the human element involved. After all, knowing the capabilities of your team and who you’re attempting to serve with data science influences heavily how to implement it.
Authors: Paolo Tamagnini & Christian Dietz
When I start a new data science project with KNIME Analytics Platform, there are always a few questions I need to ask myself before I even pull in a single node to my blank workbench.
To answer all these questions, all I need to do is ask the KNIME Hub. The KNIME Hub has been available at hub.knime.com since March 2019 but many new features have now been added with the release of KNIME Analytics Platform 4.0.
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?
Authors: Maarit Widmann (KNIME) and Alfredo Roccato (Data Science Trainer and Consultant)
Wheeling like a hamster in the data science cycle? Don’t know when to stop training your model?
Model evaluation is an important part of a data science project and it’s exactly this part that quantifies how good your model is, how much it has improved from the previous version, how much better it is than your colleague’s model, and how much room for improvement there still is.
In this series of blog posts, we review different scoring metrics: for classification, numeric prediction, unbalanced datasets, and other similar more or less challenging model evaluation problems.
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?
