MLOps is the set of practices that combines machine learning, DevOps, and data engineering to deploy, monitor, and maintain models in production reliably.
MLOps covers the lifecycle from data preparation and training to deployment, monitoring, retraining, and governance. It treats models as living artifacts that need versioning, testing, and rollback just like application code.
That operational framing matters because the industry spent years building models in notebooks and then struggling to ship them. MLOps introduces reproducible pipelines, feature stores, experiment tracking, model registries, and automated retraining so models can be updated safely without becoming brittle legacy systems.
Think of it like this. Think of a factory that not only assembles a product but also monitors how it performs in customers' homes, schedules maintenance, and feeds real-world wear data back into the next design.
A typical MLOps pipeline version-controls data and code, runs reproducible training jobs, registers models with metadata, deploys through canary or blue-green releases, monitors for drift and performance regression, and triggers retraining when thresholds are breached. Infrastructure as code and CI/CD are adapted for data and model artifacts.
"MLOps is just DevOps for models." It adds model-specific concerns such as data lineage, drift monitoring, and experiment reproducibility. "One tool solves MLOps." The space is fragmented; most teams assemble a pipeline from several specialized tools. "Automation replaces data teams." Automation removes toil, but model quality still depends on data quality and domain judgment.
Improves reliability and shipping speed for models, but requires investment in pipelines, governance, and cross-functional teams. Over-automated systems can mask data problems; under-automated systems slow iteration and increase deployment risk.