VivaciousBadger56 You’re basically answering yourself 😉 so kedro = lean feature strong community, ClearML many features small (growing) community and mlrun has a good name

Hi, just chiming in with a lesson learnt on my subreddit r/mlops - when shortlisting open-source MLOps infra, the bundled features are less important KPIs than stability and longevity markers:
community adoption active slack channel good documentation clear monetization scheme (how much does it cost if you decide to go SaaS instead of paying for own infra) - even if you never intend to go SaaS, it helps to understand if the OSS is actually “freemium” or not.
Hope that helps!

Hi VivaciousBadger56
Basically you can think of MLRun as "amazon lambda service without amazon". It is designed to run a "function" in scale on multiple nodes.
ClearML on the other hand is an MLOps platform. It does the experiment tracking, it orchestrates Task (think jobs), it does data management and lastly we recently released the serving. These are two different use cases.
Am I making sense here?

How would you compare those to ClearML?

GrittyStarfish67 : Thanks! But how are those for ClearML vs MLRun? Granted, ClearML has a ~5 times more github stars than MLRun, but besides that: Both are from mid 2019 according to releases on git. I have not been in their slack and I know nothing about community adoption. (Btw, Kedro has twice as many stars than ClearML - even if it has far fewer feature, those that it does have, seem pretty well done.)

AgitatedDove14 : Not sure: They also have the feature store (data management), as mentioned, which is pretty MLOps-y 🙂 . Also, they do have workflows ( https://docs.mlrun.org/en/latest/concepts/multi-stage-workflows.html ) and artifacts/model management ( https://docs.mlrun.org/en/latest/store/artifacts.html ) and serving ( https://docs.mlrun.org/en/latest/serving/serving-graph.html ).

GrittyStarfish67 : In terms of "has a good name" you literally mean the name or do you mean, they have a good reputation 😄 ?

Not sure: They also have the feature store (data management), as mentioned, which is pretty MLOps-y

.

Right, sorry, I was thinking about "Nuclio", my bad.

How would you compare those to ClearML?

At least based on the documentation and git state I would say this is very early stages. In terms of features they "tick all the boxes", but I'll be a bit skeptic on the ability to scale and support these features.

Taking a look at the screenshots from the docs, it also seems someone really tried to copy the ClearML experience, which tells you something about the product:
https://docs.mlrun.org/en/latest/_images/pcp.png