Argo Workflows is widely used to automate data and ML pipelines. It allows users to define multi-step processes, such as data extraction, model training, and deployment, directly in YAML files. Each step runs as a Kubernetes pod, ensuring scalability and isolation. This makes it perfect for handling large datasets or machine learning workflows that require parallel execution. Many teams use it alongside Kubeflow for seamless machine learning orchestration. Argo Workflows ensures repeatability, reliability, and full traceability, making it a great fit for both DevOps and DataOps environments.