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THE CAKE DIFFERENCE
From generic predictions to tailored,
high-impact regression models
Off-the-shelf regression models
Fast to start, hard to trust: Most prebuilt regression models miss real-world complexity and context.
- Limited to default features and no domain-specific tuning
- Difficult to integrate into production workflows or apps
- No observability into prediction quality or model drift
- Hard to meet compliance, reproducibility, or auditability standards
Regression with Cake
Custom models with full observability and control: Cake gives you the tools to build accurate, auditable regression systems at scale.
- Support for tabular, time series, and mixed data types
- Built-in evaluation, drift detection, and performance tracking
- Seamless deployment across batch, real-time, and edge environments
- Pre-integrated compliance, access control, and reproducibility
EXAMPLE USE CASES
Teams use Cake’s regression stack for a wide
range of predictive modeling tasks
Sales and revenue forecasting
Predict sales by region, channel, or product line using historical and real-time data.
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Pricing optimization
Model price elasticity and optimize dynamic pricing strategies in real time.
Medical or scientific modeling
Predict patient vitals, drug response, or sensor output with transparency and traceability.
Inventory level prediction
Forecast future inventory needs based on historical demand patterns, lead times, and seasonal trends to reduce stockouts and overstocking.
Churn risk scoring
Assign risk scores to customers based on behavior and engagement to prioritize retention efforts.
Energy load prediction
Model electricity or resource demand to optimize grid operations, reduce costs, and prevent outages.
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Scott Stafford
Chief Enterprise Architect at Ping
CEO
InsureTech Company
Felix Baldauf-Lenschen
CEO and Founder
COMPONENTS
Tools that power Cake's regression 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.
XGBoost
XGBoost is a scalable and efficient gradient boosting library widely used for structured data and tabular ML tasks.
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