The problem I see with wading into other subfields (like my own) that need high quality training datasets, is that the datasets may be proprietary, and may not really overlap that much between companies in the same industry. For example, assembly line datasets for companies making almost the same product may be vastly different. I'm really struggling to see how you can possibly achieve the same scale in other industries.
Is it weird sharing the same name as a fashion icon ;)
And I'm curious about your ML "stack". Particularly the chicken and egg problem. Are you using something like Tensorflow with pre-trained binaries, perhaps from a vendor? Or is it 100% proprietary. Thanks!
Re 2—As with most companies working on ML these days, our stack is not fully proprietary. We don't take too strong an opinion on ML framework and use both Tensorflow and Pytorch currently. We generally use neural network architectures from the literature and then iterate on top of them to suit our unique problem requirements.
If I may, can you please tell us:
As your business has grown, what has changed the most in terms of how you run it?
What were some of the biggest challenges you've overcome and any major obstacles you see in the near future for the business?
Who are your mentors?
Thanks.
Overcome many challenges, but per my last answer, building a team of the best people has been the most important and most challenging. That, and learning how to do sales ;)
Too many mentors. People in Silicon Valley are incredibly helpful. To name a few: Dan Levine, Mike Volpi, Nat Friedman, Adam D’Angelo, Ilya Sukhar, Jonathan Swanson, Albert Ni, Jeff Arnold, Charlie Cheever, and Drew Houston to name a few. I’m very very lucky.
What principles/rules did you stick to when growing your company that you thought helped improve the culture/profits?
Thanks again for acknowledging the Hacker Network community!
It’s a small thing, but it’s surprising easy to spot once you look for it. And it really matters—startups are the business of building something from nothing. You need people who believe they can bend the earth.
I'm really looking forward to more of what Scale will do in the future!
In the meantime, check out our open source datasets:
https://scale.com/open-datasets/nuscenes https://scale.com/open-datasets/pandaset https://level5.lyft.com/dataset/
Second, self-driving as a problem space will need labels for a very long time. In an application where (1) verifiable model performance is paramount, and (2) the models need to be extremely robust for cars to be safe, the need for labeled data is only magnified.
I just wanted to chime in that we're a YC company as well (S16), and I'm thankful to the HN community for having been supportive through our whole journey.