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I'm not sure there is anything extra messy about MLOps: there are lots of vendors in pretty much any area that has profit potential. If you put all vendors on a
by herdcall 5y ago
I'm not sure there is anything extra messy about MLOps: there are lots of vendors in pretty much any area that has profit potential. If you put all vendors on a chart, it will look messy, but you aren't going to be working with ALL of them (e.g., if you pick Snowflake you likely won't also be working with RedShift, Databricks...). The messy part I guess is the evaluation/selection, but not the integration or learning per se, as this article seems to imply. The article looks like a good reference for what's out there though.
- ellisv 5y agoI have an internal document just for comparing different ML vendors/frameworks/etc. Most of them have low value add, so it's easy to remove them from the picture. One challenge is whether to pick a vendor that offers solutions for each stage in the lifecycle or specializes in solutions for a particular stage. Ultimately it's a false choice because you'll run into the limitations of a vendor and need to compliment it with another. Then you have the problem of trying to staple vendor solutions together.
- nmhancoc 5y agoHi, not sure if this is question is better as a PM. I’m working on a project in this space and am curious what you find low value add about most current offerings? My project focuses on the actual deployment of the model artifact and transformation of that artifact into a callable API. Do you find most offerings low value add because you don’t want to deal with created containers (docker, etc.) (that’s my experience of most offerings in this space)? Is it because you prefer to do that work yourself? Some other reason?