MLOps
ELI5 — The Vibe Check
DevOps but for machine learning. Training a model is the easy part — keeping it running in production, monitoring its accuracy, retraining when data changes, and not accidentally deploying a broken model at 3 AM is the hard part. MLOps is the engineering discipline of making ML actually work in the real world.
Real Talk
MLOps (Machine Learning Operations) applies DevOps principles to the ML lifecycle: data versioning, experiment tracking, model training pipelines, deployment, monitoring, and retraining. Tools include MLflow, Weights & Biases, Kubeflow, and cloud-native ML platforms. It addresses the gap between ML experimentation and production deployment.
When You'll Hear This
"Our model works in a notebook but we have no MLOps to deploy it." / "MLOps is 90% of the work but gets 10% of the attention."
Related Terms
Data Pipeline
An assembly line for data. Raw data goes in one end, gets cleaned, transformed, enriched, and validated at each station, and comes out the other end ready...
DevOps
DevOps is the culture and practice of tearing down the wall between the people who write code (Dev) and the people who run it in production (Ops).
Machine Learning (ML)
Machine Learning is teaching a computer by showing it thousands of examples instead of writing out every rule.
Model
A model is the trained AI — the finished product.
Training
Training is the long, expensive process where an AI learns from data.