Machine Learning (ML) plays a critical role here at Yelp. It allows us to transform Big Data into usable information. A major area of research in ML is understanding how to apply the myriad of techniques available to an engineer. In addition to deciding on which algorithm to use, a practitioner must decide how to verify models, distribute computation, and monitor performance. Best practices exist in each of these areas, but novices may not be aware of them, and they can be tedious, even for experts. We know that many problems in computer science can be solved with another layer of abstraction, and the MLBase project is trying to do just that. The project hopes to automate the selection, implementation, and deployment of ML solutions by using “plugins” that implement different algorithms and carry meta-data about how they can be used. MLBase was the subject of our most recent meetup, organized by the SF ML meetup group.
Ameet Talwalkar, a Computer Science fellow from UC Berkeley, joined us to share his research on MLBase and a specific algorithm implemented therein. His research connects many related fields of computer science together, from algorithms research, to distributed systems, to language and API design.
In just a few short weeks, we have another mastermind joining us to speak about programming on the front end. SFHTML5 has invited Pamela Fox, the host of GirlsDevelopIt SF, another one of our monthly meetup partners, to present on how to break down HTML5 and its unequal feature distribution. We’re looking forward to seeing Pamela again and hope that you can join us as well!
Upcoming June Events:
Wednesday, June 12, 2013- 6:15PM- Learn How Disqus Does "It" When "It" Isn't Django (San Francisco Python Meetup Group)