What is MLOps?
The practice of deploying, monitoring, and maintaining machine learning models in production — the DevOps of AI.
Definition
MLOps (Machine Learning Operations) is the set of practices that bridge ML development and production operations. It covers the full lifecycle: experiment tracking, model versioning, CI/CD for models, data pipeline management, model serving, performance monitoring, and retraining. Just as DevOps transformed software deployment, MLOps has transformed how AI teams ship models reliably at scale.
Why it matters
Over 80% of ML projects that reach the prototype stage never make it to production. MLOps is what bridges that gap. Without it, models degrade silently (data drift), reproduce poorly (no experiment tracking), and take weeks to update (no deployment automation). In 2026, MLOps is table stakes for any serious AI team.
How it works
A mature MLOps stack includes: (1) data versioning (DVC, Delta Lake), (2) experiment tracking (MLflow, Weights & Biases), (3) model registry (MLflow, Hugging Face Hub), (4) serving infrastructure (BentoML, Seldon, SageMaker), (5) monitoring (Evidently, Arize), and (6) automated retraining pipelines (Airflow, Prefect). For LLMs, MLOps expands to include prompt versioning and evaluation.
Examples in practice
Automated retraining pipeline
When a model's performance metrics drop below threshold (detected by the monitoring layer), the MLOps pipeline automatically triggers retraining on updated data, runs evaluation, and deploys the new version if it passes.
A/B testing models in production
An MLOps platform routes 5% of traffic to a candidate model, collects performance data, and promotes the new model when it statistically outperforms the champion — with automatic rollback if it degrades.