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.






Overview
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 NeuralProphet, orchestrate workflows with Kubeflow Pipelines, and monitor accuracy drift over time 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
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Accelerate forecasting workflows: Build, deploy, and monitor forecasting models using pre-integrated components.
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Choose the right tools: Use the forecasting libraries and frameworks that best fit your time series and data structure.
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Run anywhere: Deploy your stack across cloud, on-prem, or hybrid environments with no lock-in.
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Monitor model accuracy: Detect forecast drift, track performance over time, and trigger automated retraining.
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Support compliance and reproducibility: Capture lineage, manage access, and enable traceability across the stack.
EXAMPLE 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.
Capacity planning
Forecast infrastructure, staffing, or manufacturing needs to ensure operations scale smoothly with demand.
Marketing performance projection
Estimate the future impact of campaigns across channels (e.g., email, paid ads, or social) using historical engagement and conversion data.
Inventory and supply forecasting
Predict stock levels and supplier needs to avoid shortages, overages, and missed revenue opportunities.
IN DEPTH
Forecast the future and act on it with predictive analytics
Forecasting is just the start. See how teams use predictive analytics to drive smarter decisions across supply chain, finance, and customer strategy, powered by real-time data and open-source AI.
BLOG
What data intelligence really means and why it matters
Data intelligence goes beyond dashboards. Learn how leading teams are using AI to connect, understand, and act on their data with speed and precision.
"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
COMPONENTS
Tools that power Cake's forecasting stack

Ray Tune
Distributed Model Training & Model Formats
Pipelines and Workflows
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
Pipelines and Workflows
Track ML experiments and manage your model registry at scale with Cake’s automated MLflow setup and integration.

Kubeflow
Orchestration & Pipelines
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
Model Evaluation Tools
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
Inference Servers
Triton is NVIDIA’s open-source server for running high-performance inference across multiple models, backends, and hardware accelerators.

Darts
Forecasting Libraries
Darts is a Python library for time series forecasting, offering a unified API for statistical, deep learning, and ensemble models.
Frequently asked questions
How does Cake support forecasting?
Cake connects open-source forecasting frameworks with secure, cloud-agnostic infrastructure. This lets teams build and deploy forecasting models faster, with built-in observability, compliance, and cost controls.
What types of forecasting can I build with Cake?
Common use cases include demand planning, revenue forecasting, supply chain optimization, financial risk modeling, and customer behavior prediction. Cake supports both short-term forecasts and long-range projections.
Why is AI forecasting better than traditional methods?
Traditional forecasting tools often rely on static assumptions and limited variables. AI forecasting adapts dynamically to new data, identifies hidden patterns, and improves accuracy over time.
How does Cake ensure security and compliance in forecasting?
With Cake, data stays within your controlled environment. The platform provides audit logs, model lineage tracking, and compliance-ready infrastructure, ensuring sensitive forecasts remain secure and meet enterprise requirements.
Learn more about Cake

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