Why Keeping a Simple Linear Regression Alive in OT Is Harder Than You Think

TL;DR

When most people think about the complexity of Artificial Intelligence in industry, they immediately picture deep neural networks, advanced architectures, or models with dozens of layers.

But the real challenge often starts much earlier — with something as “simple” as a linear regression. Let's dig in.

The Hidden Complexity of Simplicity

Imagine a model with just two parameters, running in real time, every second, 24/7 on the plant floor.

That’s:

• 1 model
• Running every 1 second
• For 30 days → 2,592,000 inferences per month

And this is before you even validate whether the prediction can be trusted.

Validating Reality: Millions of Operations

Every inference should be checked against at least five basic validations:

✔️ Is the input data present?
✔️ Is it within the expected scale?
✔️ Is the feature within range?
✔️ Is there an outlier?
✔️ Is the model still calibrated?

Even with this conservative set of 5 checks, that single regression means 13+ million operations per month — just to ensure reliability.

Now scale this up:

• Medium-sized industrial company → ~30 active models = ~400 million operations/month
• Large industrial company → ~150 models = ~1.9 billion operations/month

And this is with linear regression, one of the simplest ML techniques available.

Why Talk About Linear Regression?

Because despite its simplicity, linear regression remains one of the most powerful tools in industry.We’ve seen high-impact use cases such as:

• Virtual sensors with just one or two input variables
• Simple and robust anomaly detection
• Estimations of hard-to-measure variables in real time (e.g., NOx, CO, O₂)

It works — reliably and efficiently — especially in industries such as mining, chemicals, and pulp & paper.

But “simple” doesn’t mean “trivial.”

In OT, even a basic regression requires robust infrastructure, automation, and observability to remain trustworthy at scale.

The Real Lesson

Never underestimate the power of linear regression — nor the complexity of keeping it alive in production.

The question every industrial organization must ask is:

👉 Is your data architecture and MLOps stack ready for this?
👉 Can you guarantee performance, reliability, and security at this level of scale?

Final Thoughts

At Aignosi, this is the daily reality we face with our industrial clients. There’s no glamour in it — but making AI work in OT means ensuring that every inference, every second, is dependable.

And that requires more than algorithms. It requires industrial-grade AI infrastructure.