chaoyu_

4 ms·

1. 1.▲
   Show HN: BentoML – Unified AI Application Framework(github.com)

   2 points by chaoyu_ 3y ago|0 comments
2. 2.▲
   by chaoyu_ 3y ago

   Check out BentoML https://github.com/bentoml
3. 3.▲
   Show HN: Deploy Stable Diffusion as a Service(github.com)

   5 points by chaoyu_ 4y ago|2 comments
4. 4.▲
   Yatai: Model Deployment at Scale on Kubernetes(github.com)

   2 points by chaoyu_ 4y ago|0 comments
5. 5.▲
   by chaoyu_ 4y ago

   Most team don't really concern about the security side of things when using pickle for model deployment, more about performance and resource utilization. It works ok for very light weight models, but doesn't scale.
6. 6.▲
   by chaoyu_ 4y ago

   Regardless of the architecture, you would still need the model server for online serving use cases. And you’re right that it’s best to decouple the validation/preprocessing logic from the model runtime from architecture perspective, an
7. 7.▲
   by chaoyu_ 4y ago

   Exactly! A vertical solution focused on model serving and deployment would make more sense if it can easily integrate with the ML training platform for CICD
8. 8.▲
   by chaoyu_ 4y ago

   That’s is true, it would be more accurate to say FastAPI (or any ASGI) framework along is not enough for ML model serving, you need batching and runner for performance. And some additional Ml focused features for integration with other ML t