In recent years, the development of AI/ML models has become more accessible due to the rise of open-source models and ML development tools, leading to broader participation beyond specialized teams. Despite this democratization, challenges such as security concerns and version control remain prevalent. JFrog's new open-source plugin for MLflow addresses these issues by offering a centralized solution for storing and managing ML model experiments and ensuring secure deployment into production environments. MLflow, a popular open-source framework, aids in managing and tracking machine learning experiments, enhancing collaboration, and providing tools for smooth model development and deployment. The JFrog MLflow plugin further simplifies the process by facilitating the sharing and management of ML artifacts with JFrog Artifactory. This integration supports secure handling of open-source dependencies, governs user access, and enhances artifact management, contributing to a more efficient and secure model development process. The collaboration between JFrog and MLflow brings added benefits such as version control, access management, and CI/CD pipeline integration, streamlining workflows and ensuring the integrity of AI ecosystems.