Workflow:Mlflow Mlflow Model Logging and Registry
| Knowledge Sources | |
|---|---|
| Domains | ML_Ops, Model_Management, Model_Versioning |
| Last Updated | 2026-02-13 20:00 GMT |
Overview
End-to-end process for saving trained ML models in MLflow's standardized format, registering them in the model registry for version control, and loading them for inference.
Description
This workflow covers the full model lifecycle from training through registration and loading. MLflow's model management system packages trained models with their dependencies, inference signature, and input examples into a standardized format (MLmodel). Models can be logged using framework-specific flavors (sklearn, pytorch, transformers, openai, etc.) or the generic pyfunc flavor for custom models. The model registry provides centralized version control with aliases, tags, and stage transitions for promoting models through development, staging, and production environments.
Key capabilities:
- Framework-agnostic model packaging with 35+ flavor integrations
- Automatic dependency capture and environment specification
- Model signature enforcement for type-safe inference
- Version control with aliases and tags in the model registry
- Model loading from any registered version or run artifact
Usage
Execute this workflow when you have trained a model and need to save it in a portable, reproducible format for later inference, sharing, or deployment. Use the model registry when you need version control, stage management, or team collaboration on model promotion decisions.
Execution Steps
Step 1: Train the Model
Execute the model training process using any ML framework. The trained model object — a scikit-learn estimator, PyTorch module, Transformers pipeline, or custom Python class — will be packaged by MLflow in the next step.
Key considerations:
- Any ML framework or custom model class can be logged
- For autologging-enabled frameworks, the model may be logged automatically
- Prepare representative input data for signature inference
Step 2: Log the Model
Save the trained model to MLflow using a framework-specific log_model function or the generic pyfunc flavor. This creates an artifact containing the serialized model, an MLmodel metadata file, a conda/pip environment specification, and optionally an input example and model signature.
Key considerations:
- Use the framework-specific flavor (sklearn, pytorch, transformers, etc.) for optimized packaging
- Provide an input example for automatic signature inference
- The model signature defines expected input and output schemas for validation
- Models can be registered immediately by providing a registered model name
Step 3: Register the Model (Optional)
Register the logged model in the MLflow model registry to enable version control. Registration creates a named model entry with a new version number. Each version tracks the source run, creation timestamp, and associated metadata.
Key considerations:
- Registration can happen during log_model or as a separate step
- Each registration of the same model name creates a new version
- Tags and descriptions can be added to both models and versions
Step 4: Manage Model Versions
Organize model versions using aliases and tags. Aliases (e.g., "champion", "challenger") provide stable references to specific versions. Tags allow attaching arbitrary metadata for filtering and organization.
Key considerations:
- Aliases provide mutable pointers to specific versions
- Multiple aliases can point to the same version
- Tags support key-value metadata for version categorization
Step 5: Load Model for Inference
Load a registered model version for prediction using model URIs. Models can be loaded by version number, alias, or run artifact path. The loaded model provides a unified predict interface regardless of the original training framework.
Key considerations:
- Load by URI formats: models:/name/version, models:/name@alias, or runs:/run_id/artifact_path
- The pyfunc loader provides a framework-agnostic predict interface
- Framework-specific loaders return the native model object