Analytics

Venture Beat 3/20/2013 – Berkeley-based Wise.io has launched its first product for data scientists and developers as part of a goal to make machine learning technology more accessible. The idea for the initial “machine learning as a service” product came from the founders’ experiences on Kaggle, the website that 70,000 data scientists use to compete to solve tricky math problems.

GigaOm 3/19/2013 – In the last three decades, we’ve seen a shift in enterprise information technology, from mainframes that automated our financial information, to the client-server and web-based world that aimed to replace most paper-based processes with “systems” like CRM, ERP, e-commerce and email. And now, in the cloud era, we find ourselves on the brink of another transformative shift. This one is driven by the explosion of data and the need for traditional enterprises to find new business value through new business models and building better customer experiences.

Information Week 3/15/2013 – University of Southern California students are the latest to participate in an IBM business partner contest focused on brainstorming practical applications for the supercomputer that makes sense of huge volumes of unstructured information. This follows similar competitions with the University of Rochester and Cornell University.

High Scalability 3/13/2013 – Jason Wolfe from Prismatic has generously agreed to describe their thoroughly modern solution for answering the “what to read question” using lots of sexy words like Machine Learning, Social Graphs, BigData, functional programming, and in-memory real-time feed processing. The result is possibly even more occult, but this or something very much like it will be how we meet the challenge of finding interesting topics and stories hidden inside infinitely deep pools of information.

Genomes Unzipped 3/08/2013 – Suppose that you’ve had your DNA genotyped by 23andMe or some other DTC genetic testing company. Then an article shows up in your morning newspaper or journal (like this one) and suddenly there’s an additional variant you want to know about. You check your raw genotypes file to see if the variant is present on the chip, but it isn’t! So what next?