Machine learning's integration into a company's operations often encounters a significant hurdle when transitioning models from development to production—a process known as MLOps, which combines machine learning with DevOps practices to deploy and maintain models efficiently. Despite the initial success in building functional models, a staggering 85% fail to reach production due to the complexities of operationalization. The challenges include inconsistent deployment and performance issues, which necessitate a collaborative team comprising data scientists, ML engineers, and DevOps experts. Tools like Datagran can streamline this process by facilitating the creation and management of data pipelines, enhancing model deployment speed, and ensuring reproducibility through consistent version tracking of models and data. This approach not only optimizes efficiency but also positions companies to capitalize on innovative opportunities by maintaining a cutting-edge stance in the rapidly evolving field of machine learning operations.