the arc of software bends towards understanding
JG: Joseph Gonzalez GG: Garth Gibson (CMU) DS: Dawn Song (UC Berkeley) JL: John Langford (Microsoft NY)j YQ: Yangqing Jia (Facebook) SB: Sarah Bird M: Moderator A: Audience M: This workshop is bringing together ML and systems. Can you put your place on that spectrum? Who is your home community? YJ: Right in the middle. […]
The below is a transcript of a talk by Daniel Lo on BrainWave, at the ML Systems Workshop at NIPS'17. Deploy and serve accelerated DNNs at cloud scale. As we've seen, DNNs have enabled amazing applications. Architectures achieve SoTA on computer vision, language translation and speech recognition. But this is challenging to serve in large-scale […]
The below is a transcript of a talk by Virginia Smith on MOCHA, at the ML Systems Workshop at NIPS'17. The motivation for this work comes from the way we think about solving ML problems in practice is changing. The typical ML workflow looks like this. You start iwth dataset and problem to solve. Say […]
The below is a transcript of a talk by Alex Beutel on machine learning database indexes, at the ML Systems Workshop at NIPS'17. DB researchers think about there research differently. You have a system that needs to work for all cases. Where as in ML, we have a unique circumstance, I'll build a model that […]
The below is a transcript of a talk by Ion Stoica on Ray, at the ML Systems Workshop at NIPS'17. We've been working on it at Berkeley for more than one year. Over the past years, there's been tremendous progress in AI. Ad targeting, image&speech, many more. Many applications are based on supervised learning with […]