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I am extremely pessimistic for ML ops startup like this. At the end of the day, cloud service providers have too much of an incentive to provide these tools for
by high_derivative 6y ago
I am extremely pessimistic for ML ops startup like this. At the end of the day, cloud service providers have too much of an incentive to provide these tools for free as a cloud value add.
The other thing is that stitching together other open source tools like this is simply not enough value. Who will be incentivised to buy?
Saying this as FAANG ML org person where I see the push to open source ops tooling like this.
- DevX101 6y agoSounds like a great reason to acquire a startup like this for $XX million. Might be a great outcome if the team is lean with minimal investment.
- hobofan 6y agoWell that ship seems to have sailed with a ~$19mil investment and a 20 person (+$4mil/year burn in NYC?) team.
- minimaxir 6y agoOn paper that's the case. Google certainly has made a push toward scalable AI training and deployment. However, it is not fun to use in practice, speaking from experience. Startups beating an incumbent with substantially better UX is always a good story. Improving productivity is an easy winner for potential customers.
- wfalcon 6y agoWell... we're not really an ML ops startup haha. I am ALSO pessimistic about ML Ops startups. But calling Grid an ML Ops startup is like calling Lightning Keras... maybe at a quick blink it looks like that, but that's where the similarities end. For what it's worth, a lot of what we're building comes from my experience at FAIR.
- andrewmutz 6y agoIf it is not ML Ops, how would you describe it?
- wfalcon 6y agoit's honestly just a different approach. ML ops is adjusting your code to work with the cloud and managing all that. For us is basically integrating clouds directly into your code so the barrier disappears and the cloud providers become an extension of your laptop.
- orbifold 6y agoMight not be your target audience, but high energy physics has been operating an infrastructure like that for years: https://www.etp.physik.uni-muenchen.de/research/grid-computing/index.html https://www.etp.physik.uni-muenchen.de/research/grid-computi... You can basically use these frameworks to run your analysis jobs on any of the connected HPC centers and interactively move workloads around. The data never has to touch your hard drive either but gets moved to the compute on demand. This is how thousands of physicists do statistical analysis on petabytes of data.
- wfalcon 6y agosuper cool! One of the professors at my lab at NYU CILVR (Kyle Cranmer) i believed was super involved with this. Will definitely sync up with him! Thanks for the heads up!