Cake for Anomaly Detection
Detect outliers, fraud, and system failures in real time with Cake’s open-source, cloud-agnostic AI infrastructure. Train, deploy, and monitor anomaly detection models that scale.







Detect critical anomalies faster with modular, production-ready AI pipelines
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.
Common use cases
Teams use Cake’s anomaly detection pipelines to monitor critical systems and workflows:
Financial fraud detection
Identify irregular transactions, account behavior, or access patterns in real time across accounts and systems.
System health monitoring
Detect latency spikes, hardware failures, or service degradation using log and telemetry signals.
Customer behavior monitoring
Flag outliers in usage, engagement, or conversion rates to prevent churn or uncover new segmentation.
Components
- Training frameworks: XGBoost, LightGBM, Scikit-learn, PyTorch, TensorFlow
- Experiment tracking & model registry: MLflow
- Workflow orchestration: Kubeflow Pipelines
- Model serving: KServe, NVIDIA Triton
- Monitoring & drift detection: Prometheus, Grafana, Evidently, NannyML
- Labeling & feature stores: Label Studio, Feast
- Data sources: Snowflake, AWS S3
"Our partnership with Cake has been a clear strategic choice – we're achieving the impact of two to three technical hires with the equivalent investment of half an FTE."

Scott Stafford
Chief Enterprise Architect at Ping
"With Cake we are conservatively saving at least half a million dollars purely on headcount."
CEO
InsureTech Company