Machine Learning Operations (ML Operations or MLOps) is an engineering discipline that focuses on unifying machine learning system development (MLSD) and machine learning system operations (MLSO). It seeks to provide an end-to-end framework for developing, deploying, and monitoring models in production.

MLOps can be thought of as an extension of DevOps principles to the world of machine learning. Here’s what you might typically find in an MLOps process:

1. Development: Model development involves selecting the right algorithms, tuning hyperparameters, and validating the model against a validation dataset.
2. Versioning: Tracking different versions of datasets, models, and other assets to ensure reproducibility and traceability.
3. Testing: This includes automated testing to validate models and ensure that they meet the required standards.
4. Deployment: Models are deployed to production environments where they can be accessed by other systems and users. This might be through APIs, batch processes, or embedded directly into applications.
5. Monitoring: Continuous monitoring of models in production to ensure they are performing as expected and to detect any drift in the underlying data.
6. Collaboration: Enabling seamless collaboration between data scientists, engineers, and other stakeholders.
7. Compliance and Security: Implementing security measures and complying with regulations that pertain to the use and handling of data.
8. Automation: Creating automated pipelines that handle the entire lifecycle of machine learning models, from data ingestion to deployment.