Cake for Time-Series Analysis

Overview

Time-series data is everywhere—from sensor logs and financial transactions to user activity and supply chain metrics. But extracting insights from it reliably and at scale takes more than a good model. It requires robust preprocessing, flexible training, repeatable pipelines, and always-on monitoring.

Cake delivers a full time-series analysis stack that’s built on open-source tools and optimized for enterprise AI workflows. You can ingest, clean, and align time-based data using AirByte or DBT, build models with frameworks like Darts, PyTorch, or XGBoost, and deploy workflows using Kubeflow Pipelines. With built-in experiment tracking, observability, and compliance, your time-series pipelines don’t just work—they scale.

And because Cake is modular and cloud agnostic, you can integrate the latest open-source advances while avoiding the cost and rigidity of managed AI platforms.

Key benefits

• Accelerate time-series workflows: Move from data ingestion to model deployment with reusable, pre-integrated tools.
  
  
• Use the best open-source models: Train with Darts, Neural Prophet, PyTorch, and more—no black boxes required.
  
  
• Deploy across environments: Run pipelines on any cloud, hybrid setup, or edge environment.
  
  
• Detect issues before they scale: Monitor real-time data streams and trigger retraining as patterns shift.
  
  
• Reduce cost and risk: Avoid vendor lock-in and manage sensitive time-series data on your own infrastructure.