Machine learning operations (MLOps) are a set of best practices for running machine learning models successfully in production environments. Data scientists and system administrators have expanding options for setting up their pipeline. However, while many tools exist for preparing data and training models, there is a lack of streamlined tooling for tasks like putting a model in production, maintaining the model, or monitoring performance. Enter vetiver, an open-source framework for the entire model lifecycle. Vetiver provides R and Python programmers with a fluid, unified way of working with machine learning models.

Machine learning operations (MLOps) are a set of best practices for running machine learning models successfully in production environments. Data scientists and system administrators have expanding options for setting up their pipeline. However, while many tools exist for preparing data and training models, there is a lack of streamlined tooling for tasks like putting a model in production, maintaining the model, or monitoring performance. Enter vetiver, an open-source framework for the entire model lifecycle. Vetiver provides R and Python programmers with a fluid, unified way of working with machine learning models.