KubeCon + CloudNativeCon Europe 2025

Picture this! You are deploying an application on a cloud platform, and you want to ensure seamless performance for the application from day one. Early anomaly detection is crucial for identifying issues before they escalate and maintaining system reliability. Ideally, you will leverage historical data to train an ML model for real-time anomaly detection. However, the complexity of training and deploying ML models makes them impractical at launch. What if you could skip training and still spot anomalies in your application health metrics the moment your system is live?

In this session you’ll learn about the benefits of using pre-trained ML models for day one anomaly detection. We’ll discuss how to deploy lightweight, unsupervised pre-trained models using cloud-native tools like Kubeflow for model fine-tuning. Attendees will learn techniques to setup and refine models to detect anomalies and observe application health from the first deployment.