Integrating the JFrog Platform with Databricks provides organizations with a robust solution for transitioning machine learning models from experimentation to production while ensuring security and governance. This process involves a five-step approach that includes safely downloading models using JFrog Artifactory to mitigate security risks, storing proprietary models in a centralized and secure registry, and packaging them for deployment using JFrog ML to create standardized container images. JFrog Xray provides automated security scanning of these images, ensuring vulnerabilities are identified and managed before deployment. The final step involves deploying the model as a scalable API, either through existing infrastructure or using JFrog ML's hosting solutions, which offer advanced deployment strategies and autoscaling capabilities. This integration enables data science teams to maintain their workflow in Databricks while providing MLOps and security teams with the necessary tools for secure, traceable, and efficient model deployment.