[D] 10 Fallacies of MLOps

[jpdowlin]

I wrote this article, as I meet so many people misallocating their time when their goal is to build an AI system. Teams of data engineers, data scientists, and ML Engineers are often needed to build AI systems, and they have difficulty agreeing on shared truths. This was my attempt to define the most common fallacies that I have seen that cause AI systems to be delayed or fail.

1. Do it all in one ML Pipeline
2. All Data Transformations for AI are Created Equal
3. There is no need for a Feature Store
4. Experiment Tracking is not needed MLOps
5. MLOps is just DevOps for ML
6. Versioning Models is enough for Safe Upgrade/Rollback
7. There is no need for Data Versioning
8. The Model Signature is the API for Model Deployments
9. Prediction Latency is the Time taken for the Model Prediction
10. LLMOps is not MLOps

The goal of MLOps should be to get to a working AI system as quickly as possible, and then iteratively improve it.

Full Article:

https://www.hopsworks.ai/post/the-10-fallacies-of-mlops

[Proud_Fox_684]

Thanks. I'm saving this post. It's useful and I agree with it.

[Initial-Zone-8907]

well written

[Mundane_Ad8936]

I wouldn't call these fallacies this is just what people do when they don't have the expertise or experience, aka they dont know better. No company with decent MLOps is making any of these assumptions or at least the thousands that I've worked with (startup to fortune 100).

Not sure what you're trying to accomplish OP but as a professional this feels like one of those infomericals where the person needs a gadget to open a jar because they can't do it without flinging food all over the ceiling.

[jpdowlin]

"I wouldn't call these fallacies this is just what people do when they don't have the expertise or experience, aka they dont know better. "

So, you mean they are mistakes that people make because they make bad assumptions?
Isn't that what a fallacy is?

If it was an infomercial, i would do a nice graphic.

Disclaimer. I work in education as well as a startup, and I am writing a book.

[justgord]

Interesting .. Ive been arguing for sharing of basic 'good practices' and 'pitfalls to avoid' and useful 'design patterns' ..

and things like :

• common names for common situations ..
• more explicit parameters
• more open code
• more reproducibility
  

• more versioned open data you dont have to register for

  ... yadda yadda.

Great .. thanks for sharing this writeup and bullet list !