Cake for Regression

Overview

Regression models fundamentally support predictive analytics, from revenue forecasts to patient vitals. However, developing these models at scale and deploying them into production involves more than just a Jupyter notebook and a few Python libraries. You need consistent data workflows, reliable model serving, and observability that can stand up to real-world variability.

Cake provides a modular, open-source regression stack that handles the full lifecycle: training with frameworks like XGBoost or PyTorch, orchestrating workflows with Kubeflow Pipelines, and monitoring performance with tools like Prometheus and Evidently. Everything is composable, cloud-agnostic, and easy to evolve as your use case grows.

By eliminating infrastructure bottlenecks and gluing together best-in-class components, Cake helps teams go from prototype to production faster, with full control over models, data, and deployment environments.

Key benefits

• Accelerate development and deployment: Go from notebook to production with pre-integrated regression tools.
  
  
• Choose your stack: Use the best tool for each task with modular, open-source components.
  
  
• Run anywhere: Deploy across clouds and environments without lock-in.
  
  
• Monitor with confidence: Track performance, detect drift, and trace predictions from input to output.
  
  
• Stay compliant by default: Meet enterprise security, lineage, and auditability requirements out of the box.