I'd suggest giving this a look at: https://docs.neuro-ai.co.uk it's a pay for compute time API. It wraps AI frameworks instead of hosting. Pretty new so a lot of support needs to be added, but good for the basics.
If you are into ML research on-premise you the best-of-bread approach is better to sell internally. That's basically happens in my team rn.
Personally I just started a new web app pet project. There is no way I will go down the rabbithole of hacking a backend API together myself. So went with Firebase + GCP APIs (I didn't choose Amplify bcoz AWS UI sucks and I don't care about IT salesman arguments)
I think your notes around building an ML infrastructure are interesting. As I evaluate my team's roadmap, deciding which direction to spend resources on is challenging - there seems to be a lot of noise in the data infrastructure/ML space as a whole right now - new platforms, functionality, and new entrants.
Appreciate your continuing voice on this - it's helping guide the path forward.
I'd suggest giving this a look at: https://docs.neuro-ai.co.uk it's a pay for compute time API. It wraps AI frameworks instead of hosting. Pretty new so a lot of support needs to be added, but good for the basics.
If you are into ML research on-premise you the best-of-bread approach is better to sell internally. That's basically happens in my team rn.
Personally I just started a new web app pet project. There is no way I will go down the rabbithole of hacking a backend API together myself. So went with Firebase + GCP APIs (I didn't choose Amplify bcoz AWS UI sucks and I don't care about IT salesman arguments)
I think your notes around building an ML infrastructure are interesting. As I evaluate my team's roadmap, deciding which direction to spend resources on is challenging - there seems to be a lot of noise in the data infrastructure/ML space as a whole right now - new platforms, functionality, and new entrants.
Appreciate your continuing voice on this - it's helping guide the path forward.