[D] What do people dislike the most about modern MLOps startups?

Submitted by Impressive_Ad4945 t3_xuciyb in MachineLearning

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Although the MLOps space is dominated by startups, the journey is not easy. The startup ecosystem influences different MLOps operations. However, the acceptance of MLOps startups is not easy and seamless. In my opinion, the reasons are data challenges, post-services support, and growing competition. Please suggest your thoughts on what people hate about modern MLOps startups.

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sanderbaduk t1_iqvi17b wrote

  • Aggressive marketing
  • High pricing (per-user and such nonsense) which is often not clearly indicated on a website
  • Tools that fall over with a ton of bugs and errors when you do anything other than the demo script
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LSTMeow t1_iqwvkrk wrote

Funny that you say aggressive marketing, I mod r/MLOps and it's been tough. Even this post by an obvious throwaway has a similar one (removed) on the sub, meaning that this post itself is part of the strategy.

the post

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Impressive_Ad4945 OP t1_iqvk02x wrote

Data challenges and post deployment support add complexities. Thanks for your inputs.

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darkshenron t1_iqvjkp4 wrote

  • Over committing and under delivering
  • Trying to be a product company when they should really be a service company (or a hybrid)
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summitlandscapes t1_iqwm8ym wrote

Your second point is spot on and maybe even too generous. Some aren't even ready to be a service provider, but believe they have an innovation and think that should be enough of a business model :/

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carlthome t1_iquzf0i wrote

Do you work at a MLOps startup?

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Impressive_Ad4945 OP t1_iqv19jb wrote

Yes

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Tgs91 t1_iryz3ac wrote

I'll give you a real answer:

Most startup companies over hype their products and just flat out aren't very valuable to my workflow. Usually the problems they "solve" are the easiest parts of my job that I can do in 10 minutes with an open source python package. Or they build some software around a solution that is 2-3 years out of date and only applies to cookie cutter problems. And when questioned about problems that aren't their basic simplistic use cases, they often aren't even aware of the flaws in their methods. I don't trust the tools until after I read their backend code to double check their claims, and at that point I might as well have implemented it myself.

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trnka t1_iqx68a8 wrote

Most of the ones I've talked to solve fairly small problems, and it just wasn't worth the hassle of going through multiple months of procurement process for them and/or making them follow our DevSecOps team's requirements. There are some bigger scoped companies like Databricks, but the monetary cost wasn't worth it for us even if it would've been worth the procurement hassle.

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sayhisam1 t1_irfbi75 wrote

It's hard to understand the value proposition. Often in ML there is so much jargon that hides that the MLOps tool is just a glorified data visualizer on the cloud..

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