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Sounds like fairly generic deployment infrastructure that has nothing to do with machine learning. But why pass up the opportunity to use a buzzword to get on
by tus88 7y ago
Sounds like fairly generic deployment infrastructure that has nothing to do with machine learning.
But why pass up the opportunity to use a buzzword to get on the front page of HN?
- SQueeeeeL 7y agoI blame us for upvoting them
- calebkaiser 7y agoThis is a good point, and it’s something we think about a lot. In one sense, you can certainly think of Cortex as a tool for deploying/scaling/monitoring Python functions on AWS, but there are many ML-specific features that make it different. It does things like prediction monitoring, and it supports models exported by ONNX and TF Serving. We also have designed Cortex to prioritize infrastructure needs specific to inference workloads (inference workloads are read only and memory hungry, for example). This is why we’ve prioritized things like GPU spot instances. Our long-term plan includes more end-to-end ML workflows, including things like training, but for now we’re focused on getting model serving right.
- RandyRanderson 7y agoI count at least 3: ML, Go, Python. Challenge: could they fit more in there under the title len limit?