4. You won’t need to update your models as much One mindboggling fact about DevOps: Etsy deploys 50 times/day. Netflix 1000s times/day. AWS every 11.7 seconds. MLOps isn’t an exemption. For online ML systems, you want to update them as fast as humanly possible. (5/6) https://twitter.com/chipro/status/1310952553459462146
What part is scary? I think they’re missing out on the sheer variety of model usage—probably as variable as software deployments. But I don’t think there’s anything particularly scary about any given point on the curve.
Some really do get built, validated, and deployed twice a year. Some have CI pipelines that re-train with new data and re-validate every few minutes. Some are self-updating, and re-sync to a clean state periodically. Some are running continuous a/b tests of many candidate models, picking the best-performer for a customer segment every few minutes, and adding/removing models from the pool many times per day.
Whelp… that’s scary:
Chip Huyen
@chipro
Replying to
@chipro
4. You won’t need to update your models as much One mindboggling fact about DevOps: Etsy deploys 50 times/day. Netflix 1000s times/day. AWS every 11.7 seconds. MLOps isn’t an exemption. For online ML systems, you want to update them as fast as humanly possible. (5/6)
https://twitter.com/chipro/status/1310952553459462146
What part is scary? I think they’re missing out on the sheer variety of model usage—probably as variable as software deployments. But I don’t think there’s anything particularly scary about any given point on the curve.
Some really do get built, validated, and deployed twice a year. Some have CI pipelines that re-train with new data and re-validate every few minutes. Some are self-updating, and re-sync to a clean state periodically. Some are running continuous a/b tests of many candidate models, picking the best-performer for a customer segment every few minutes, and adding/removing models from the pool many times per day.