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THE CAKE DIFFERENCE
From static segments to intelligent
clustering at scale
Manual segmentation
Static rules that don’t scale: Predefined customer or behavior segments often miss subtle patterns or new trends.
- Relies on fixed heuristics like age, region, or purchase tier
- Misses emergent behavior, edge cases, and mixed signals
- Requires ongoing manual updates to keep relevant
- Difficult to scale across datasets, teams, or domains
Clustering with Cake
Uncover structure in your data—automatically: Cake gives you tools to build and deploy clustering workflows across use cases and modalities.
- Supports k-means, DBSCAN, hierarchical, and embedding-based clustering
- Works across tabular, vector, and time series data
- Built-in evaluation, visual inspection, and cluster drift detection
- Deploy clusters into downstream pipelines or applications with full traceability
EXAMPLE USE CASES
Teams use Cake’s clustering stack to identify
patterns and groupings in large,
unlabeled datasets
Customer segmentation
Group users by behavior, engagement, or preferences to personalize campaigns and product experiences.
Product or content categorization
Automatically cluster items by metadata, content, or usage to improve search and recommendations.
Asset management
Identify patterns across devices, logs, or sensor streams to inform inventory or maintenance planning.
Identifying emerging customer personas
Uncover previously unrecognized user groups based on evolving behavior or preferences to inform product and messaging strategy.
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Grouping support tickets to streamline operations
Cluster incoming tickets or issues by topic, sentiment, or urgency to prioritize and automate customer service workflows.
Optimizing territory and resource planning
Use location or usage-based clustering to improve how sales regions, delivery zones, or field teams are structured and deployed.
Scott Stafford
Chief Enterprise Architect at Ping
CEO
InsureTech Company
Felix Baldauf-Lenschen
CEO and Founder
COMPONENTS
Tools that power clustering on Cake
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.
scikit-learn
scikit-learn is a foundational Python library for machine learning that includes tools for classification, regression, clustering, and more.
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