The paper investigates the prediction capabilities of a Machine Learning model in real-time scheduling applications on Kubernetes in a serverless computing environment with the aim to achieve a degree of energy efficiency. A highly pluggable framework for integrating a learning-based model into the Kubernetes scheduler is proposed and evaluated in a serverless setup on OpenFaaS. The experimental results in a cloud-native deployment demonstrate that, while maintaining Quality of Service for the application, an overall 8% in power reduction is achieved at a minimal performance loss.