The open source machine learning (ML) revolution is accelerating, with projections that by 2027, over 90% of new business software applications will include ML models or services. Organizations can successfully navigate the open source and proprietary ML landscape by adopting MLOps processes informed by DevOps lessons, such as using clear versioning schemas, caching artifacts to guard against instability, and ensuring compliance with licensing agreements. Platforms like JFrog offer ML model management capabilities that support these processes by providing tools for traceable versioning, artifact caching, and licensing enforcement. Additionally, it is crucial for enterprises to evaluate the maturity and reliability of open source ML repositories by considering factors like the number of contributors and recent updates. These practices help mitigate risks associated with open source projects, ensuring effective, secure, and efficient ML model adoption.