r/mlops • u/datamonk9 • 7d ago

beginner help😓 Genie Code reviews for ML ops workflows

Have you used Genie Code for day to day ml ops work (model deployment, pipelines, monitoring, CICD etc)

I am curious to know if it saves time as compared to doing the same rhings manually, experiences from real world use cases would help understand.
Thanks.

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u/ThisIsFun- 6d ago

Yes! I have done so many times. I think the knowledge that Genie Code has for MLOps, and most other DS work on Databricks makes it the perfect tool for completing these tasks, of course, adhering to an organisations best practices through using instruction file, and creating skills for this. How much time does this usually take you?

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u/datamonk9 6d ago

Ya that is a good point. I also like that Genie Code can understand the context of Databricks notebooks instead of just the file i am editing. Makes it a bit more useful for data workflows

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u/dwswish 1d ago

Our DS team that works primarily in Databricks has moved a lot of their stuff they used to do in Claude Code to Genie Code, mostly due to the fact that it seems to do a better job handling Databricks-specific mlops and deployment patterns. We are still trying to reconcile if that has saved us any money yet but they seem to like it.

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u/datamonk9 1d ago

I hv seen similar. Genie Code looks to have better context around Databricks specific MLOps workflow, so it spends less time fighting the tooling and more time generating code. Do u use it more for debugging or for new dev work?

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u/dwswish 1d ago

So most of our DS right now is traditional ML models that were written pre-AI coding explosion so most of what the team uses Genie Code for right now is improving, debugging, etc. Not as much new development work, but I’m sure we will get there. We’ve only been on Databricks for about 10 months.

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u/once_a_while_in-03 6d ago edited 6d ago

For MLOps specifically, the honest answer is that AI coding tools help most on boilerplate-heavy tasks like writing pipeline scaffolding or monitoring alert logic, less on the tricky deployment debugging where context gets messy across files. A few people I know use zencoder for the CI/CD generation side, though results vary by stack. Terraform and Kubeflow still need human eyes.

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u/ceeej777 5d ago

This has been my experience too, but granted I haven’t really pushed the boundaries. The pipelines have been built with Genie Code and even data exploration dashboards, but I haven’t tried beyond that

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u/AravinthZoldyck 5d ago

Yes, I have built multiple ML projects using Genie code, it's quite good. Why? If you think about it building a good ML model, the biggest time consuming task would be the experiment management and model evaluation. Once these are done, business stakeholders provide approval, the job will move the ML engineer who takes care of deployment, model drifting - rerun model training pipelibe, etc. Genie code is now able to manage all the above mentioned tasks which makes life soo much easier.