Day 2: MLOps vs. DevOps: Mapping the Unique Challenges of Non-Deterministic Systems

Traditional software is predictable.
Machine learning systems are not.

In this demo, we break down why MLOps is fundamentally different from DevOps and why applying classic DevOps thinking to ML systems leads to silent failures, degraded performance, and lost business value.

This is the day you stop thinking “ML is just code”.

What This Demo Covers

✔️ Why ML systems are inherently non-deterministic
✔️ Data drift vs concept drift (and why both matter)
✔️ Why “the service is up” doesn’t mean “the model works”
✔️ How model accuracy decays without throwing errors
✔️ Why reproducibility in ML is harder than in software
✔️ How production ML systems must be adaptive, not static

Видео Day 2: MLOps vs. DevOps: Mapping the Unique Challenges of Non-Deterministic Systems канала Hands On Course Demo