Breaking Changes in MLflow 3
MLflow 3 introduces several breaking changes as part of our commitment to improving the framework's consistency, performance, and maintainability. This guide will help you understand what's changing and how to update your code accordingly.
Core Framework Changes
MLflow Recipes Removal
What's changing: MLflow Recipes (previously known as MLflow Pipelines) has been completely removed from MLflow (#15250).
Why: MLflow Recipes was deprecated in previous versions as the team refocused on core MLflow functionality and more modern machine learning workflows.
How to migrate: If you're using MLflow Recipes, you'll need to migrate to alternative workflow management solutions. Consider using standard MLflow tracking and model registry functionality directly in your workflows, and see if MLflow Projects will work better for your use cases.
AI Gateway Configuration Changes
What's changing: The 'routes' and 'route_type' config keys in the gateway server configuration have been removed (#15331).
Why: The AI Gateway configuration has been simplified and modernized to better support current deployment patterns.
How to migrate: Update your AI Gateway configuration to use the new configuration format. Check the MLflow 3 documentation for the updated gateway configuration syntax.
MLflow Deployment Server Removal
What's changing: The MLflow deployment server application and the start-server
CLI command have been removed (#15327).
Why: MLflow has evolved its deployment strategy to better support modern serving architectures.
How to migrate: Use MLflow's built-in model serving capabilities with mlflow models serve
or containerized deployment options. For more complex deployments, consider using integration with cloud providers or platforms like Kubernetes.