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Paperspace

includes Paperspace Gradient

5 statements across 1 episodes · 0 bullish · 1 bearish · 1 people on the record · first statement Feb 25, 2019 by Dillon Erb · said 5 times in 1 episodes since 2019 · across every show →

Mentions by year, the whole family

brought up most by Dillon Erb (5)

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2019 5 mentions in 1 episode

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Everything said about Paperspace, oldest first

Feb 25, 2019
Disclosure
Paperspace customers run machine learning training workloads lasting up to three weeks
“We run workloads for customers that can go up to like three weeks for a training task”
Dillon Erb Feb 25, 2019 ▶ 9:11 CI/CD for Machine Learning & AI // Dillon Erb, Paperspace (FirstMark's Data Driven NYC)
Feb 25, 2019 neutral
Insight
Deep learning teams using standard code branches are light years ahead
“If you find, you know, companies that are doing deep learning that have, you know, staging development and master branches, that's already, like, light years ahead of, I would say, 99% of the companies that will be using this technology in five years.”
Dillon Erb Feb 25, 2019 ▶ 20:03 CI/CD for Machine Learning & AI // Dillon Erb, Paperspace (FirstMark's Data Driven NYC)
Feb 25, 2019 negative
Insight
Lack of standard model repositories forces companies to hack solutions using Dropbox
“There's no good model zoo or model repo, and, you know, almost every company that I've worked with has their own version of that, and it can range from a Dropbox folder to, you know, actually repurposing Travis to actually do that.”
Dillon Erb Feb 25, 2019 ▶ 16:37 CI/CD for Machine Learning & AI // Dillon Erb, Paperspace (FirstMark's Data Driven NYC)
Feb 25, 2019 neutral
Prediction Not checkable as stated
Erb predicts ten new machine learning hardware accelerators will launch in 2019
“My guess is by the end of the year, there will be 10 more new devices that are coming out.”
Dillon Erb Feb 25, 2019 ▶ 8:23 CI/CD for Machine Learning & AI // Dillon Erb, Paperspace (FirstMark's Data Driven NYC)
Feb 25, 2019 neutral
Assertion Not checkable as stated
Erb: The largest deep learning teams generally do not exceed 20 people
“The biggest teams generally don't go much larger than, say, 15 to 20 people, especially in the deep learning space.”
Dillon Erb Feb 25, 2019 ▶ 14:25 CI/CD for Machine Learning & AI // Dillon Erb, Paperspace (FirstMark's Data Driven NYC)
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