Build MLOps Pipelines Using Jenkins, Docker & K8s
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MLOps pipelines are the backbone of modern machine learning operations, ensuring models are reliably built, tested, deployed, and maintained at scale. Combining tools like Jenkins , Docker , and Kubernetes (K8s) offers a powerful way to automate the entire ML lifecycle—from code integration to containerization and production deployment. This article guides you through the process of building a scalable MLOps pipeline using these three core technologies, helping you streamline your ML workflows in both development and production environments. Build MLOps Pipelines Using Jenkins, Docker & K8s Why Jenkins, Docker, and Kubernetes? Each tool in this stack plays a critical role in enabling automation, repeatability, and scalability: · Jenkins : A popular open-source Continuous Integration/Continuous Delivery (CI/CD) automation server. It builds and tests code automatically · ...