alando46
Joined 10 years ago
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72
Submissions
68
Member since
May 6
Recent submissions
Comment · 3 days ago
thanks for the feedback!
Comment · 2 years ago
Gg hashicorp
Comment · 3 years ago
Problem for our use case is saving on gpus is pointless if we have to keep paying egress fees for our 250 TB training dataset.
The single interface for any cloud GPU is cool, but hard to imagine it taking off without some additional features.
I think for lots of shops the hardest part isn't the compute but moving the data around. Ie for us, we use s3, some lustre caching and spot instance nodegroups. We are a deep learning research team that spends roughly 40-50k/month on aws compute for training jobs. I imagine this is somewhat mid tier, maybe more than some but certainly far less than others.
For inference, data egress costs could be less of an issue, but your service would really need to be almost invisible. It probably would be pretty complicated for a number of reasons, but if you could design a "virtual on-demand nodegroup"™ that I could add to my existing clusters and then map to whatever k8s stuff I want, that would probably be useful. I would need to be able to auto deploy a base image to the machine and then provision the node and register with my cluster.
Just some unorganized thoughts. Good luck and have fun.
Comment · 5 years ago
is there an obvious best way to try sending today using some sort of digital message?
Comment · 7 years ago
Lol duh hahaha
Comment · 7 years ago
Student loans reinforce existing socioeconomic boundaries.
Effectively, if a student can't afford to pay tuition and living expenses upfront then the cost of a college education is 15-50+% more than for a student who comes from a wealthy family. How is this equal opportunity?
Comment · 7 years ago
Radiant Earth Foundation | DevOps Engineer | Oakland, CA | Onsite
Radiant Earth Foundation's core mission is to make insights from remote sensing data and machine learning an order of magnitude more accessible for global non-profits, humanitarian organizations, emerging economy governments, and others. https://radiant.earth
We provide a free platform that takes care of all of the geospatial processing and machine learning so that users with any level of remote sensing experience, from zero to expert, can use near real-time data to get geospatial insights for their use case.
In addition to our platform, we work with organizations on complex use cases with high-impact outcomes. Since our founding in 2016, we've worked with the Bill and Melinda Gates Foundation (a primary funder), the Omidyar Network (another primary funder), as well as the United Nations Institute for Training and Research, Schmidt Futures, the World Bank and more.
We work with a range of fellows and contractors to support our efforts, but our in-house engineering/ML team is just 2 people :) In addition to being somebody who is excited to be part of a small team working on large-scale problems (right now we're all generalists, to an extent), we're especially interested in applicants who want to help us foster a team culture of respect, inclusion, and diversity.
Apply here: https://bit.ly/2CLLzdK
If you have any questions, please don't hesitate to shoot me an email: alando (at) radiant.earth