Machine learning returned to the center of attention last week for the second time in a row after a little-known startup called SigOpt Inc. raised $2 million in funding from two of Silicon Valley’s most prominent investors to put a horizontal twist on the technology. Its namesake engine promises to automate the tedious process of training analytic algorithms in identifying specific data patterns.

SigOpt’s customers are applying that functionality to everything from optimizing the layout of their websites to testing complex chemical formulas, with the number of use cases only multiplying as time passes and more organizations join the bandwagon. The new capital from Andreessen Horowitz and Data Collective will help expand the appeal of the technology even further as machine learning continues to gain traction.

But it’s not only SigOpt that’s capitalizing on the trend. The day after the announcement of its funding round, another machine learning startup called Whetlab Inc. became part of Twitter Inc. in a deal that will see its five-person team come aboard and contribute to its internal analytics efforts. The former Harvard, University of Toronto, and Université de Sherbrooke researchers bring with them valuable expertise in processing the kinds of massive data volumes that the social networking giant works with.

The advanced functionality that the likes of SigOpt and Whetlab are developing may not be accessible for the average business worker quite yet, but that’s changing rapidly. The latest milestone towards ubiquitous analytics came in the form of the new filters that Google added to its cloud-based spreadsheeting service last week for finding specific details within large datasets.

Users can then incorporate those tables into one of the several new chart templates  introduced in conjunction and set up access controls to prevent accidental editing.  The restrictions appear as notifications that the search giant sees coming just as useful for self-reminding as warning colleagues.

Photo via r2hox