The shift in DevOps from traditional CI/CD practices to Git-based management has enhanced software scalability and collaboration by using Git as a single source of truth for infrastructure as code. However, the complexity of machine learning operations (MLOps) requires distinct approaches due to unique challenges like data versioning and model training. Pachyderm emerges as a key tool for integrating data version control within MLOps, allowing seamless synchronization with GitHub Actions to automate machine learning workflows. This integration simplifies the deployment of machine learning models, ensuring that both code and data remain in perfect sync, which increases the reliability of these models. By using Pachyderm and GitHub Actions, engineers and data scientists can automate and streamline their machine learning processes, unifying versioned code with versioned data for consistent and efficient deployments.