Building an MLOps pipeline with Dagger.io and KitOps

Over 85% of machine learning models never reach production due to the disconnect between data scientists, ML engineers, and DevOps engineers. MLOps pipelines address this by integrating version control, CI/CD, model monitoring, and integration testing. Dagger.io offers a way to define pipelines as code and integrate CI/CD and monitoring, while KitOps simplifies the packaging and management of model dependencies. This guide provides steps to create an ML pipeline using these tools, from setting up prerequisites to integrating with CI/CD platforms like GitHub Actions.