MLOps (MLOps)

DevOps looked at machine learning and said “you need me,” and honestly, it was right. MLOps is the practice of applying all those hard-won lessons about CI/CD, monitoring, and reproducibility to the wild world of machine learning models. Because it turns out that training a model in a Jupyter notebook and emailing the serialized file to your ops team is not, in fact, a production-ready workflow.

MLOps covers the entire model lifecycle: data versioning, experiment tracking, model training pipelines, deployment, monitoring for drift, and retraining. It’s the acknowledgment that a model in production is not a static artifact — it’s a living thing that will slowly degrade as the real world drifts away from whatever data you trained it on. Your fraud detection model from 2023 might think everything in 2026 is fraud. Or nothing is. Either way, you’ve got a problem.

Why it matters: Without MLOps, organizations end up with models that work brilliantly in a notebook demo and mysteriously fail in production. MLOps brings engineering rigor to data science, ensuring that models are reproducible, monitorable, and replaceable — because the only thing worse than a bad model is a bad model that nobody knows how to retrain.