> alluding to with respect to the ONNX being an Azure on ramp
Yeah sorry, should've worded it better in the original article. ONNX is one of the industry's first attempt at standardization of ops across various deep learning frameworks. Here's a way to think about it: Like Google, Microsoft has a cloud business. Unlike Google, it doesn't have a deep learning framework (Tensorflow) and is a significant threat if the cloud players continue to vertically integrate their ML stack. So ONNX makes sense, ie. you, the ML engineer should convert to ONNX so now at least you can run it on multi cloud providers!
Tangential to this, there's also a rise in heterogenous environments, and picking the "best" is non-trivial. A lot of serving / inference hosting companies capitalize on this, and ONNXRuntime is sort of Microsoft (Azure)'s answer: "give me an ONNX or PyTorch (which we will subsequently convert to ONNX), and I'll show you how easy it is to deploy it to Azure". The key nuance here is how much user tooling friction there is to deploying models, and it's in the best interest of these cloud providers to sometimes use their open source initiatives as on-ramps to their larger cash generating products / services.
