Last March, (for the fifth time, according to Forest Grove Technology), the Knime Analytics platform was named a Gartner Magic Quadrant leader. This year's other leaders are Alteryx, SAS, RapidMinder, and H2Oai. The best thing I learned from the announcement? Knime is open source, and free for individual users—I can afford to look at it!
Knime (silent "k"; rhymes with "dime") provides a graphical user interface to chain together blocks that represent steps in a data science workflow. (So they're like Pentaho or Informatica but for machine learning. Or LabView if you have an engineering background.)
It has dozens of built-in data access and transformation functions, statistical inference and machine learning algorithms, PMML, and custom Python, Java, R, Scala, a zillion other nodes, or other community plugins (since it's open source, anyone can make a plugin.) Even better, Knime imposes structure and modularity on a data science workflow by requiring code fit into specified building blocks.
This post implements the Bayesian NFL model from last month in Knime. It adds the upstream and downstream workflows to pull new data each week and write the model output to a spreadsheet: enough for a first look at this tool.