The integration of DevOps and MLOps into a unified software supply chain is increasingly essential, yet it presents significant challenges due to the distinct characteristics of traditional software and machine learning models. These challenges include managing model dependencies, adapting CI/CD tools for machine learning needs, and ensuring security throughout the lifecycle of software and models. Successful integration requires addressing data dependencies, ensuring compatibility of frameworks and libraries, and incorporating security measures to protect data and models from vulnerabilities. Best practices for overcoming these hurdles involve adopting standardized tooling, using centralized feature stores for data consistency, designing modular and extensible CI/CD pipelines, and embedding security and compliance from the start. By fostering collaboration among data science, engineering, and operations teams through a unified development environment, organizations can streamline their operations, enhance model accuracy, and maintain compliance, ultimately creating a cohesive and resilient software supply chain that meets the demands of both traditional and machine learning deployments.