MLOps
MLOps, short for machine learning operations, connects model development with deployment and ongoing operation. It uses practices such as versioning, testing, automated releases and monitoring to make machine learning systems repeatable and maintainable.
Also known as: machine learning operations
A team building a support-ticket classifier needs more than a trained model file. It records the training data and code versions, checks predictions on held-out tickets, and packages the model with the same text preprocessing used during training. A release can then be reproduced and compared with its predecessor.
After deployment, the team checks incoming data and prediction outcomes. A new product name or changed support policy may make an old model less useful even when its code still runs. Monitoring provides a reason to investigate; it does not automatically establish that retraining will fix the problem.
This matters because a machine learning system includes data pipelines and serving software as well as learned parameters. LLMOps applies related practices to language-model applications, adding concerns such as prompt versions, retrieval and tool traces. Neither label replaces task-specific evaluation or clear responsibility for a release.
Sources
- Google Cloud: MLOps, continuous delivery and automation pipelines — Explains versioned development, data and model testing, deployment, monitoring and training-serving differences.
Go deeper
- Microsoft Learn: Introduction to MLOps course
Follow a learning path on experimentation, pipelines, deployment and monitoring.