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Differences Between Shipping Classic Software and Operating ML Models with a Lead MLOps Engineer at TMNL Simon Stiebellehner, and neptune.ai CEO Piotr Niedzwiedz

ML Platform Podcast

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NEPT and ASPE Stick

I think that is a super important step they are taking simply because if you have a more abstract layer that allows you to easily plug in other executing engines, well, that gives you a lot of flexibility. For me, it's also painful because I'm looking on MLOps from software development perspective as it's my background. If we wouldn't have generic more infrastructure agnostic pipeline framework, it will limit a little bit what we can build on top of that. But for really mature platforms, you always want to think about adding at least a thin layer on top whatever external tool you're using.

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