Beyond the Buzz: Why MLOps Deserves Its Own Line in Your Tech Due Diligence

Beyond the Buzz: Why MLOps Deserves Its Own Line in Your Tech Due Diligence

Everyone’s chasing AI. Few are ready to run it at scale.

At Opsintell, we’ve seen it too often: a startup proudly touts its LLM-powered magic, demo looks slick, deck brags about model accuracy. But under the hood? No versioned datasets, no automated model retraining, no monitoring. In short: no MLOps.

And that’s a problem.

MLOps ≠ DevOps

Let’s get this straight: MLOps isn’t just DevOps for AI. It’s Dev plus data, plus models, plus statistical drift, plus governance.

Unlike software code, ML models degrade with time, even if your codebase doesn’t change. Why? Because the world does. Data shifts. Behavior evolves. Models silently decay.

So if you’re doing technical due diligence on an AI-powered company and treating it like any other SaaS platform, you’re flying blind.

What We Look For in an MLOps Audit

At Opsintell, when we assess an ML-powered stack, we apply a dedicated lens across the full ML lifecycle:

• Data lineage & versioning
  Can they track what data was used, when, and how it changed? Is it reproducible?
• Model training & experimentation
  Do they have a governed way to compare experiments? Or is it all hidden in Jupyter notebooks?
• Deployment & monitoring pipelines
  Is model serving continuous, testable, and rollback-capable? Are they tracking drift?
• CI/CD adapted for ML
  Are they integrating model validation in their release flows, with checks on both performance and fairness?
• Governance & security
  Can they trace a prediction back to a model version and dataset? Are there audit trails for regulators?

In short: we treat ML as a production-grade lifecycle, with its own risks, hygiene, and scale-readiness indicators.

Why This Matters to Investors

Two reasons:

1. Model risk = valuation risk
   A non-governed model in production can misbehave silently. That’s not just a bug, it’s liability.
2. AI-washing is rampant
   Founders know that “AI-native” increases their multiple. But VCs need to distinguish true defensibility from gimmick. That means evidence, not just ambition.

Opsintell’s Take

If your target startup builds on ML, the diligence must too.
Treating AI like a black box, or worse, a marketing bullet, means missing critical operational risk.

We don’t just ask if they use AI.
We check how they manage it, scale it, and future-proof it.

Because in 2025, MLOps is not a nice-to-have, it’s infrastructure.