Mole End

Some blog posts are for the ages, others are ephemeral. The Mole acknowledges this post's place in the latter category, yet still feels it appropriate to write today about what he heard yesterday even if it is relevant only until tomorrow.

Last night's meetup — yes, the third this week — was hosted at Yelp under the auspices of SF Bayarea Machine Learning; Ameet Talwalkar demonstrated the slideware version of MLBase.  One of the lessons the Mole learned at some point between late academia and early industry was to speak only of what he had done, not what he was going to do.  Practitioners like and want and need to know what has already been accomplished, preferably accompanied with a link to download that version of the code.  A broad agenda to build a better mousetrap is greeted with skepticism.  So while the Mole wishes the MLBase team all the best, he will reserve judgment about the efficacy of their remarkable new universal can opener until he can install it somewhere.

Oddly and ironically, the Mole noted some small evidence for one of the bluntly stated propositions of the talk the previous day.  The speaker there alleged that the machine learning algorithms in open source tools invariably lag the state of the art.  The algorithms promised for the first release of MLBase fit that pattern; the open question is whether they will be only a starting point or representative of what the system will deliver.  

Expect a comment later this summer when the first public version of MLBase is released.  The Mole hopes to be proven wrong in his doubts.