Cake for Business Forecasting
Predict future trends using open-source forecasting frameworks integrated with Cake’s modular, cloud-agnostic AI platform. Build pipelines that scale from experimentation to enterprise-grade production.







Deliver accurate, auditable forecasts with flexible, production-ready infrastructure
From revenue planning and inventory management to churn prediction and energy demand, forecasting powers some of the most business-critical decisions. But building forecasting models that are reliable, explainable, and production-ready isn’t easy, especially when data pipelines and model infrastructure are stitched together manually.
Cake provides a unified, open-source forecasting stack that handles everything from data ingestion and transformation to model training, versioning, and performance monitoring. Use trusted tools like XGBoost, PyTorch, or Neural Prophet, orchestrate workflows with Kubeflow Pipelines, and monitor accuracy drift over time—all within a modular, cloud-agnostic system.
Cake allows you to expand forecasting from standalone notebooks to fully auditable production systems, all while maintaining flexibility and control.
Key benefits
- Accelerate forecasting workflows: Build, deploy, and monitor forecasting models using pre-integrated components.
- Choose the right tools: Use the forecasting libraries and frameworks that best fit your time series and data structure.
- Run anywhere: Deploy your stack across cloud, on-prem, or hybrid environments with no lock-in.
- Monitor model accuracy: Detect forecast drift, track performance over time, and trigger automated retraining.
- Support compliance and reproducibility: Capture lineage, manage access, and enable traceability across the stack.
Common use cases
Teams use Cake’s forecasting stack to power real-time and long-term decision-making:
Demand forecasting
Predict product demand by location or SKU to optimize inventory, pricing, and supply chain planning.
Churn modeling
Forecast user churn likelihood based on activity, cohorts, and seasonality to improve retention strategies.
Revenue and usage projections
Model future usage or revenue for planning, capacity forecasting, and budgeting purposes.
Components
- Training frameworks: XGBoost, LightGBM, Scikit-learn, PyTorch, TensorFlow, Neural Prophet, Darts
- 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