MLOps is a discipline that aims to unify machine learning development and operations by applying DevOps principles to the machine learning lifecycle. It encompasses practices for collaboration, automation, and monitoring of machine learning workflows, from data preparation and model training to deployment and maintenance. MLOps facilitates continuous integration and continuous delivery (CI/CD) for machine learning models, allowing teams to rapidly iterate and improve models while ensuring consistent performance in production environments.
Key components of MLOps include:
- version control for datasets and models,
- automated testing
- monitoring of model performance to detect data drift and model degradation over time.
By implementing MLOps, organizations can enhance collaboration between Data Scientist and operations teams, reduce time to market for machine learning applications, and improve the reliability and scalability of their machine learning solutions.