CI/CD practices, originating from DevOps, are increasingly being adopted in MLOps to streamline the deployment and management of machine learning applications. This article explores how four different teams have utilized CI/CD concepts and tools to enhance their machine learning workflows across various industries, such as retail, marine, logistics, and financial services. These teams have implemented CI/CD using different tools like Azure DevOps, Jenkins, AWS CodePipeline, and Google Cloud's Vertex AI, adapting traditional methods to suit ML needs by automating tasks, ensuring model quality, and maintaining robust pipelines. The article highlights the emergence of ML-native pipeline tools, such as Kubeflow and Vertex AI Pipelines, which can offer more tailored solutions for machine learning projects by integrating seamlessly with ML workflows and reducing the operational burden of traditional CI/CD tools. It suggests that while traditional CI/CD tools are still in use, ML-native tools are becoming more favorable for achieving faster and more reliable delivery of machine learning products.