====== Model Registry ======
The **Model Registry** is a core component of the Databricks Machine Learning platform that provides centralized management and governance for machine learning models throughout their lifecycle. It serves as a critical infrastructure layer for organizations implementing production machine learning systems, particularly in regulated industries such as banking and finance where model tracking and compliance documentation are essential requirements (([[https://www.databricks.com/blog/banks-dont-have-ai-problem-they-have-data-platform-problem|Databricks - Banks Don't Have an AI Problem, They Have a Data Platform Problem (2026]])).

===== Overview and Core Functionality =====
The Model Registry functions as a comprehensive model management system that tracks approved model versions and maintains detailed records of model deployments, performance metrics, and governance metadata. By integrating with Delta Lake, Databricks' ACID-compliant data lakehouse format, the Model Registry ensures that all model artifacts, training data lineage, and prediction logs maintain consistency and auditability. This integration enables organizations to maintain complete model provenance and audit trails required for regulatory compliance (([[https://www.databricks.com/blog/banks-dont-have-ai-problem-they-have-data-platform-problem|Databricks - Banks Don't Have an AI Problem, They Have a Data Platform Problem (2026]])).

The platform manages models across multiple stages of the deployment lifecycle, including development, staging, and production environments. Each model version tracked in the registry includes metadata such as training parameters, feature specifications, performance baselines, and approval status. This structured versioning approach prevents model drift and ensures that production systems operate with explicitly validated and approved model versions.

===== Integration with Model Monitoring =====
A key capability of the Model Registry is its integration with **Model Monitoring**, which enables continuous observation of model behavior in production environments. This integration captures three critical data streams: model predictions, input features, and actual outcomes. Together, these data streams support real-time detection of model performance degradation, data drift, and concept drift—conditions where the statistical properties of input data or the relationship between inputs and targets change over time (([[https://www.databricks.com/blog/banks-dont-have-ai-problem-they-have-data-platform-problem|Databricks - Banks Don't Have an AI Problem, They Have a Data Platform Problem (2026]])).

By maintaining continuous observability through this integrated monitoring approach, organizations can identify when model performance falls below acceptable thresholds and trigger retraining workflows or model rollbacks. The captured prediction logs and outcome data create a comprehensive audit record essential for regulatory compliance in industries such as banking, where models making credit, lending, or risk assessment decisions must maintain documented performance characteristics.

===== Regulatory Compliance and Governance =====
For regulated industries, the Model Registry provides essential governance infrastructure. The comprehensive logging of model inputs, predictions, and outcomes supports compliance with frameworks such as the Fair Lending Rule, which requires documentation of disparate impact analysis for credit decisions, and SOX (Sarbanes-Oxley) requirements for internal control documentation over financial reporting systems. The registry's version control and approval workflow capabilities enable organizations to demonstrate that only validated, approved models operate in production systems.

The integration of model metadata, performance metrics, and prediction logs within a Delta Lake-based system ensures that compliance documentation remains immutable and auditable. This technical foundation supports regulatory examination processes where auditors require evidence of model governance, performance monitoring, and documented decision-making processes.

===== Model Lifecycle Management =====
The Model Registry implements a structured approach to model lifecycle management, enabling organizations to orchestrate model transitions from development through retirement. Teams can use the registry to define custom model stages, implement approval workflows requiring stakeholder sign-off, and automate deployment pipelines that ensure only approved models reach production. This structured lifecycle approach reduces operational risk by preventing untested or deprecated models from serving production traffic.

The registry supports collaborative workflows where data scientists, machine learning engineers, and business stakeholders can review model performance characteristics, examine feature importance, and make informed decisions about model promotion. Documentation capabilities within the registry enable teams to capture domain knowledge, known limitations, and intended use cases for each model version.

===== See Also =====

  * [[databricks_unity_catalog|Databricks Unity Catalog]]
  * [[mlflow|MLflow]]
  * [[model_monitoring|Model Monitoring]]
  * [[databricks|Databricks]]
  * [[databricks_marketplace|Databricks Marketplace]]

===== References =====