The need for robust MLOps frameworks has become paramount in a world increasingly reliant on AI. Current solutions range from proprietary cloud services to independent open-source components, each with advantages and limitations. This paper presents a Kubernetes-based, open-source MLOps framework designed to streamline the lifecycle management of machine learning models in production environments. It integrates a comprehensive suite of open-source tools compatible with Python, covering all aspects from development, testing, deployment, and monitoring to updating models, reducing the need for human intervention. Finally, we compared state-of-the-art MLOps tools and frameworks, demonstrating that our framework meets the same features as proprietary options, such as Amazon SageMaker.
