Answered raw tape
D 5 · C 5 · P 5 · Cm 5 5.00
Q a question about that because one of the things that I've always thought about when it comes to this tension between academia and industry, you might hear about needs and requirements from industry, but when you're in research environment, you have unlimited open-ended, you know, vision and an algorithm and a research paper is very different than something in production at industry scale. So how do you guys navigate that?
A First of all, students, they do internships, they understand It's a problem. They develop the first solutions. And then with that understanding, you can start more principled designs to build these systems. But the truth is that in every successful project, we had at least one or two partners. For instance, uh, for, um, we work very closely initially with Facebook. When we started working with Facebook, Facebook, you know, has an entire, uh, cluster, big cluster for big data. It was 80 nodes. And their big data team was like three people. Then, for instance, in the case of Mesos, we work very closely with Twitter. And Hinman went to Twitter and worked very closely with Twitter engineer to deploy Mesos in production. And actually, the feedback from Twitter has a big impact on the Mesos evolution. From just supporting big data, cluster computing frameworks like Hadoop, it went to support These long running services.
AI assessment note: “worked very closely with Twitter engineer to deploy Mesos in production.”
Answered raw tape
D 5 · C 5 · P 5 · Cm 4 4.85
Q a question about that because one of the things that I've always thought about when it comes to this tension between academia and industry, you might hear about needs and requirements from industry, but when you're in research environment, you have unlimited open-ended, you know, vision and an algorithm and a research paper is very different than something in production at industry scale. So how do you guys navigate that?
A First of all, students, they do internships, they understand It's a problem. They develop the first solutions. And then with that understanding, you can start more principled designs to build these systems. But the truth is that in every successful project, we had at least one or two partners. For instance, uh, for, um, we work very closely initially with Facebook. When we started working with Facebook, Facebook, you know, has an entire, uh, cluster, big cluster for big data. It was 80 nodes. And their big data team was like three people. Then, for instance, in the case of Mesos, we work very closely with Twitter. And Hinman went to Twitter and worked very closely with Twitter engineer to deploy Mesos in production. And actually, the feedback from Twitter has a big impact on the Mesos evolution. From just supporting big data, cluster computing frameworks like Hadoop, it went to support These long running services.
AI assessment note: “in every successful project, we had at least one or two partners.”
