CI/CD in MLOps: Deploying Models Faster and Smarter
Introduction CI/CD in MLOps is changing how machine learning models reach production in 2025. Today, AI teams cannot depend on slow manual deployment methods. They need automation, speed, and reliability. Continuous Integration and Continuous Deployment help achieve that. CI/CD builds, tests, and deploys ML models just like software, but with extra steps for data, retraining, and monitoring. As models become central to business success, CI/CD pipelines ensure faster updates, better quality, and trusted results. Organizations now see MLOps as a core function for AI success. Pipelines run on cloud platforms, handle real data, and support real-time ML applications. CI/CD ensures that no manual mistakes slow projects. Many professionals start learning with MLOps Training programs to build strong foundations on automated ML workflows and CI/CD tools. CI/CD in MLOps: Deploying Models Faster and Smarter What CI/CD Means in MLOps CI/CD is not new, but in machine l...