MLOps, or Machine Learning Operations, enhances collaboration among data scientists by automating processes such as testing, versioning, and tracking. As MLOps is still developing, practitioners often build tools from scratch, with some help from DevOps, although these can be generic and require custom code. GitHub Actions have been introduced as a solution, integrating data science and machine learning workflows with software development processes. An example includes using GitHub Actions to orchestrate machine learning pipelines, track experiments, and report results to pull requests. The flexibility of GitHub Actions allows for diverse workflow compositions, such as adding links to mybinder.org on pull requests. The ecosystem of MLOps and data science GitHub Actions is expanding, with tools for orchestrating ML pipelines, running Jupyter Notebooks, and tracking experiments. The community is encouraged to contribute new Actions, with ideas like data versioning and model deployment, and can refer to GitHub Actions documentation for guidance.