Machine Learning as a Service at Scale, a history from the trenches
BigML pioneered ML platforms back in the day: it was 2011 and only Google's blackbox predictions were offering something similar to an API-based ML service. It was also the hype time of Big Data and Hadoop, when everybody first installed a big cluster with complex infrastructure and, afterwards, wondered what problem they were going to solve. This is the story of how we side-stepped the hype, kept our small start-up focused, and faced the many technical challenges of building a scalable, easy-to-use, API-first, DSL-based platform aimed at making ML accessible to everyone.
Jao is part of the founding team of BigML. Before he joined back in 2011, he was hacking for Oblong, previously he worked for Google, and previous to that, he worked on embedded software development for the scientific payload of LISA Pathfinder, secure cryptographic voting protocols in Scytl and personalisation systems at iSOCO. He was a theoretical physicist in a previous life, and wrote a Ph. D. thesis on gravitational wave detectors. He also got a bachelor’s degree in computer science.