Un data science pipeline framework est essentiel pour mener à bien des projets de machine learning. Il structure les étapes, facilite la collaboration et garantit la reproductibilité. Dans ce contexte, Kedro s’impose comme un outil puissant pour les data scientists. Grâce à sa structure modulaire, il permet de concevoir, maintenir et déployer des workflows ML […]
Machine learning (ML) projects often involve complex workflows, from data preprocessing and model training to deployment and monitoring. Managing these workflows efficiently is crucial for success. This is where Kedro-MLflow comes into play — a powerful integration of Kedro, a workflow orchestration tool, and MLflow, a platform for managing the ML lifecycle.
In this article, we’ll explore the Kedro-MLflow documentation (available as a PDF here) and highlight how this integration can simplify and enhance your ML workflows.