Cake for Anomaly Detection

Overview

From fraud prevention and equipment failure to customer behavior shifts, anomaly detection is essential to operational visibility. But detecting rare or unexpected events at scale requires more than just a model—it demands reliable data pipelines, customizable models, and always-on monitoring.

Cake provides a composable, cloud-agnostic stack for anomaly detection. Train models using frameworks like PyTorch, LightGBM, or Scikit-learn. Track experiments with MLflow, deploy with KServe, and monitor for drift using tools like Prometheus and Evidently—all within Cake’s orchestrated, modular infrastructure.

With Cake, you can quickly go from exploratory analysis to production-ready anomaly detection pipelines that are scalable, auditable, and responsive to real-world conditions.

Key benefits

• Accelerate deployment of anomaly detection systems: Use pre-integrated tools to build, test, and launch faster.
  
  
• Customize for your data: Choose frameworks and detection strategies that best match your signal patterns and domains.
  
  
• Run securely and scalably: Deploy across any environment (cloud, hybrid, or on-prem) with full control and visibility.
  
  
• Monitor and retrain automatically: Track detection accuracy and trigger pipeline updates when data shifts.
  
  
• Enable compliance and auditability: Capture full lineage and ensure traceability for high-stakes domains.