Merging DevOps best practices with MLOps presents an opportunity to unify machine learning and traditional software supply chains, enhancing operational efficiency, accelerating release cycles, and improving cross-team collaboration. This integration reduces redundancy in infrastructure and processes, optimizes resource allocation, and centralizes tools and workflows. By extending CI/CD practices to include machine learning, organizations can automate the entire lifecycle, reducing manual intervention and ensuring rapid deployment of both software code and ML models. The unified approach facilitates better communication and collaboration between engineering, data science, and operations teams, breaking down traditional silos and promoting shared accountability. Technical integration involves treating ML models as artifacts, enabling version control, artifact storage, and dependency management alongside software components, and adapting CI/CD pipelines to handle ML-specific tasks like model training and validation. This cohesive system ensures that both software and models are developed, validated, and deployed efficiently, ultimately enhancing an enterprise's competitive edge and responsiveness to market demands.