Databricks Model Registry: Versioning, Staging & Deployment

Machine learning models are never static—they evolve with new data, updated features, and improved algorithms. Managing multiple versions, testing them in stages, and deploying reliably to production can be complex and error-prone.

Databricks Model Registry simplifies this process by providing a centralized platform for model versioning, staging, and deployment, enabling teams to collaborate, track, and deploy models efficiently.

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Why Model Registry Matters

Imagine a team building a predictive model for customer churn:

• Multiple data scientists experiment with different algorithms.
• Each model version must be tested, validated, and approved before production deployment.
• Without a registry, tracking versions and ensuring reproducibility is challenging.

Databricks Model Registry addresses these issues by offering:

• Versioning: Track every model iteration
• Staging & Production Lifecycle: Promote models safely across stages
• Collaboration: Share models and metadata across teams
• Auditability: Track who created, approved, and deployed each model

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How Model Registry Works

1. Register Model: Log trained models in MLflow and register them in the registry.
2. Version Models: Each update or retraining creates a new version.
3. Stage Promotion: Move models through stages like Staging → Production.
4. Deploy Models: Serve models via Databricks Model Serving or export for external deployment.
5. Monitor & Govern: Track usage, performance, and access across teams.

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Example: Registering and Versioning a Model

import mlflow
from sklearn.ensemble import RandomForestClassifier
import pandas as pd

# Sample training data
X = pd.DataFrame({"feature1": [1, 2, 3], "feature2": [4, 5, 6]})
y = [0, 1, 0]

# Train model
model = RandomForestClassifier()
model.fit(X, y)

# Log and register model in MLflow
with mlflow.start_run() as run:
    mlflow.sklearn.log_model(model, "customer_churn_model", registered_model_name="CustomerChurn")

Result: The model is now versioned automatically in Model Registry:

Model Name	Version	Stage	Created By
CustomerChurn	1	Staging	data.scientist
CustomerChurn	2	Production	data.scientist

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Example: Promoting a Model to Production

from mlflow.tracking import MlflowClient

client = MlflowClient()
client.transition_model_version_stage(
    name="CustomerChurn",
    version=1,
    stage="Production",
    archive_existing_versions=True
)

Effect: Version 1 is now live in production, while any previous production versions are archived.

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Example: Deploying Model via Model Serving

import requests
import json

endpoint_url = "https://<databricks-instance>/model/CustomerChurn/1/invocations"

# Sample input for inference
input_data = {"features": [1, 4]}

response = requests.post(endpoint_url, headers={"Authorization": "Bearer <TOKEN>"},
                         data=json.dumps(input_data))
print(response.json())

Example Output:

{
  "prediction": 0
}

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Key Benefits of Databricks Model Registry

Feature	Benefit
Model Versioning	Track every model iteration for reproducibility
Stage Management	Safely promote models from Staging to Production
Collaboration	Teams can share models, metadata, and performance metrics
Deployment Integration	Seamless deployment with Model Serving or external systems
Governance & Auditing	Monitor model lineage, approvals, and usage

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Summary

Databricks Model Registry streamlines the ML lifecycle, ensuring version control, stage promotion, and reliable deployment. By centralizing models and their metadata, teams can collaborate efficiently, maintain reproducibility, and deploy with confidence, reducing risk and accelerating AI-driven outcomes.

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The next topic is “Databricks AI SQL Functions — AI_GENERATE, AI_QUERY, AI_CLASSIFY”.