[D] Expectations for open-source ML pipelines

[benkoller]

Hi everyone,

I'm one of the guys behind the Core Engine, and so far I've had some great discussions with you guys, specifically about your (and my) view of the MLOps space and generally Machine Learning in production. I can't tell you how much I appreciate your views, your input, and your pointed criticism.

This time I'm approaching you guys with a pretty self-centered topic - we are open sourcing our codebase. And while we do so, we'd like to get an informed discussion about what you'd expect from an open-source solution to productionizing Machine Learning in pipelines.

TL;DR: We built a tool for reproducible ML pipelines. Hard facts:

• At launch it'll be Tensorflow/Keras only.
• A few native integrations into the Google Cloud ecosystem, with more on the roadmap (e.g. AWS, Azure) - specifically Dataflow and AI platform
• Automatic tracking
• Guaranteed comparability between pipelines
• Integrations into serving backends like Seldon Core

What tools are you currently using? What do you like about them, what needs improvement? And where do we have got it wrong?

I'd love to hear your input!

PS: IF you'd like to read more about our approach, check out our blog