Based on this thread, the comparison should include
* metaflow (model training on AWS Batch) * polyaxon (model training on kubernetes) * pachyderm (experimentation) * hopsworks (model training/serving/ and more, mostly on kubernetes) * cortex (model serving on kubernetes) * seldon-core (model serving and monitoring on kubernetes)
and likely more that I missed.
I can see why it would be so hard to put together this comparison.
Even with all these tools, there is still a lot of manual work for data scientists or DevOps engineers the data scientists pass their models off to.
It also seems there is yet to be a fully open source DevOps stack. Most companies still build custom software to glue together manual processes (like integrations between different tools for training, deploying, monitoring, etc). This could be one factor why more comparisons of these tools and stack discussions have not been more popular - they can't share them yet.