MLOps (Machine Learning Operations) is the set of practices that apply DevOps principles to the lifecycle of machine learning models: versioning code, data and models, automating training and deployment, monitoring quality and drift in production.
It addresses two concrete problems. Many models stay stuck at the prototype stage for lack of a deployment pipeline. And those that reach production degrade silently as real-world data drifts away from the training data.
Common tools are MLflow, Kubeflow, Vertex AI, SageMaker, Weights & Biases, Metaflow and DVC for data versioning. LLMOps is its counterpart for LLM-based applications.
Why it matters when hiring
MLOps becomes a hiring topic when several models run in production and Data Scientists spend more time deploying than modeling. The right profile often comes from DevOps or data engineering with a real ML culture. Strong signal: they set up drift detection and retraining, not just a deployment pipeline.