Model Registry Integration

Ray Serve is Python-native, which means it integrates seamlessly with the broader MLOps ecosystem. You can easily connect Ray Serve deployments to Model Registry, enabling production-ready ML workflows without complex configuration or glue code. This guide shows you how to integrate Ray Serve with Model Registry to build end-to-end ML serving pipelines.

Why Python-native integration matters

Unlike framework-specific serving solutions that require custom adapters or complex configuration, Ray Serve runs arbitrary Python code. This means you can:

Load models directly from any model registry using standard Python clients
Combine model loading and inference in a single deployment
Iterate quickly without wrestling with YAML configurations or custom serialization formats

(mlflow-serving-intig)=

Integrate with MLflow

MLflow is a popular open-source platform for managing the ML lifecycle. Ray Serve makes it easy to load models from MLflow Model Registry and serve them in production.

Best practices for serving MLflow models

Use model signatures and input schema validation: Always log a model signature using mlflow.models.infer_signature so MLflow can validate inputs. This prevents silent failures when upstream code changes and enables automatic schema enforcement during serving.
Package dependencies explicitly: Use pip_requirements when logging models and pin versions of core libraries. This ensures your model behaves identically across training, evaluation, and serving environments.
Persist preprocessing pipelines: If you use scikit-learn, log complete Pipeline objects that include preprocessing steps. This ensures training and serving transformations stay aligned.
For LLMs and diffusion models, use Hugging Face Hub or Weights & Biases: MLflow's built-in REST server isn't optimized for high-concurrency GPU workloads. For large language models, diffusion models, and other heavy transformer-based architectures, use Hugging Face Hub or Weights & Biases as your model registry. These platforms provide better tooling for large model artifacts, and Ray Serve handles GPU batching, autoscaling, and scheduling efficiently.

Train and register a model

The following example shows how to train a scikit-learn model with best practices and register it with MLflow:

{literalinclude}

:language: python
:start-after: __train_model_start__
:end-before: __train_model_end__

This function trains a RandomForestRegressor wrapped in a Pipeline with preprocessing, logs the model with a signature and pinned dependencies, and registers it in MLflow Model Registry with the name sk-learn-random-forest-reg-model.

Load and serve the model

Once you've registered a model in MLflow, you can load and serve it with Ray Serve. The following example shows how to create a deployment that loads a model from MLflow Model Registry with warm-start initialization:

{literalinclude}

:language: python
:start-after: __deployment_start__
:end-before: __deployment_end__