This 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

This 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.