MLOps

AI’s impact on CD/CD pipelines is growing, but successful integration requires understanding key challenges like data drift, model versioning, training times, hardware needs, and complex monitoring. Applying DevOps principles, especially automation, continuous integration, and collaboration, is crucial. MLOps extends DevOps to manage models and datasets. Designing a continuous deployment pipeline involves data ingestion/validation, model training/versioning, automated testing, staging, production deployment, and monitoring. Dedicated development teams offer long-term benefits. Best practices include versioning everything, comprehensive testing, containerization, automated retraining, integrated monitoring, role-based collaboration, and scalability planning.