Everyone’s chasing AI. Few are ready to run it at scale.
I’ve seen it too many times: a startup proudly touts its LLM-powered magic. The demo looks slick, the deck brags about model accuracy. But when I look under the hood?
- No versioned datasets
- No automated retraining
- No monitoring of drift
In other words: no MLOps.
And that’s a problem for both investors and founders.
MLOps ≠ DevOps
Let me be clear: MLOps is not just DevOps for AI.
It’s Dev + Data + Models + Drift + Governance.
Unlike software code, ML models degrade with time even if the codebase doesn’t change. Why? Because the world does:
- Data shifts
- User behavior evolves
- Statistical drift creeps in
If diligence treats an AI startup like any other SaaS platform, you’re flying blind.
What I Look For in an MLOps Audit
When I run a technical due diligence on a company that leans on ML, I apply a dedicated lens across the full lifecycle:
- Data lineage & versioning
Can they track which data was used, when, and how it changed? - Model training & experimentation
Is there a governed way to compare experiments or is everything hidden in Jupyter notebooks? - Deployment & monitoring pipelines
Is serving continuous, testable, and rollback-capable? Do they track drift? - CI/CD adapted for ML
Are performance and fairness checks integrated into release flows? - Governance & security
Can they trace a prediction back to a model version and dataset? Is there an audit trail for regulators?
This is the hygiene that separates marketing bullets from production-grade AI.
Why This Matters to Investors
From my perspective, the link is direct:
- Model risk = valuation risk.
A model that silently decays in production isn’t just a bug, it’s liability. - AI-washing is rampant.
Founders know “AI-native” inflates multiples. But VCs need evidence, not ambition.
Skipping an MLOps lens in diligence means risking overpayment for brittle AI or worse, discovering liabilities post-deal.
My Take
I don’t just ask if a startup uses AI.
I check how they manage it, scale it, and future-proof it.
Because in 2025, MLOps isn’t a nice-to-have. It’s infrastructure.
And ignoring it in diligence isn’t just oversight, it’s risk.
