.png?width=220&height=168&name=Group%2010%20(1).png)
THE CAKE DIFFERENCE
From noisy alerts to precise, production-ready
anomaly detection
Basic thresholding or black-box tools
Static rules and mystery models: These systems generate alerts—but rarely the ones that matter.
- Hard-coded thresholds miss subtle or dynamic shifts
- High false-positive rates overwhelm teams and reduce trust
- No transparency into why something was flagged
- Difficult to tune or adapt to evolving patterns
Anomaly detection with Cake
Accurate, adaptive, and fully explainable: Cake lets you build anomaly detection workflows that evolve with your data and scale with your systems.
- Support for univariate, multivariate, and time series anomalies
- Built-in evaluation, drift detection, and false-positive tuning
- Trace every alert back to inputs, thresholds, and model decisions
- Deploy in batch or streaming environments with full observability
EXAMPLE USE CASES
Where teams are implementing
anomaly detection with Cake
.png?width=125&height=125&name=money%20(1).png)
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.
Sensor and IoT alerting
Identify abnormal readings from industrial sensors, medical devices, or smart infrastructure to prevent downtime or hazards.
Anomalous model behavior detection
Identify unexpected shifts in model outputs (e.g., confidence scores, prediction distributions, or usage patterns) that may indicate drift, bugs, or misuse.
Data pipeline quality checks
Identify anomalies in row counts, schema drift, or distribution shifts across data pipelines to catch broken jobs or corrupted inputs early.
.png)
.png)
Scott Stafford
Chief Enterprise Architect at Ping
CEO
InsureTech Company
Felix Baldauf-Lenschen
CEO and Founder
COMPONENTS
Tools that power Cake's anomaly detection stack
Ray Tune
Ray Tune is a Python library for distributed hyperparameter optimization, built on Ray’s scalable compute framework. With Cake, you can run Ray Tune experiments across any cloud or hybrid environment while automating orchestration, tracking results, and optimizing resource usage with minimal setup.
MLflow
Track ML experiments and manage your model registry at scale with Cake’s automated MLflow setup and integration.
Kubeflow
Kubeflow is an open-source machine learning platform built on Kubernetes. Cake operationalizes Kubeflow deployments, automating model training, tuning, and serving while adding governance and observability.
Evidently
Evidently is an open-source tool for monitoring machine learning models in production. Cake operationalizes Evidently to automate drift detection, performance monitoring, and reporting within AI workflows.
NVIDIA Triton Inference Server
Triton is NVIDIA’s open-source server for running high-performance inference across multiple models, backends, and hardware accelerators.
Darts
Darts is a Python library for time series forecasting, offering a unified API for statistical, deep learning, and ensemble models.
The Best Open-Source Anomaly Detection Tools
Your business generates a constant stream of data, and hidden within it are clues about what’s working and what’s about to break. Manually sifting...
