MLOps
MLOps (Machine Learning Operations) is the set of practices, processes, and tools used to develop, deploy, monitor, maintain, and manage machine learning models throughout their lifecycle.
What is MLOps?
MLOps applies DevOps principles to machine learning while addressing the specific challenges of data, models, and continuous training. It covers activities such as data and model versioning, automated testing, deployment, monitoring, retraining, and workflow management across development and production environments.
Why is MLOps Important?
MLOps helps organizations make machine learning development and deployment more consistent, scalable, and reliable. It provides greater control over model lifecycles, reduces manual processes, and helps teams detect issues such as model degradation and data drift after deployment.
Common use cases
MLOps is commonly used for model deployment, continuous training, model monitoring, ML orchestration, version control, automated testing, and managing production machine learning systems.