What is Perpetual ML?
Perpetual ML is a unified machine‑learning studio that connects natively with Snowflake and upcoming Databricks, keeping data within the warehouse to preserve security and governance. It offers automated training through PerpetualBooster, continual learning that reduces training time from O(n²) to O(n), and direct optimization of user‑defined business objectives such as profit maximization or risk minimization.
The platform tracks, compares, and reproduces all experiments, stores models in a secure, version‑controlled registry, and deploys them for batch or real‑time inference from a single web interface. Integrated monitoring captures metrics, data drift, and model drift without the need for retraining or ground truth.
Perpetual ML pricing Freemium
Verify on the official pricing page.
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Perpetual ML's key features
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Automatic model training
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Batch-time reduction
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Business objective optimization
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Experiment tracking & comparison
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Version-controlled model registry
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Real-time metrics monitoring
Perpetual ML use cases
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Automate churn‑prediction modeling for a subscription platform by running continuous training pipelines directly in Snowflake, automatically adjusting the model as new usage data arrives and flagging data‑drift events to keep retention strategies cost‑effective.
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Deploy a real‑time credit‑risk scoring model for a financial institution with Perpetual ML’s built‑in monitoring, secure model registry, and Snowflake integration, enabling instant loan approvals while tracking model performance against regulatory compliance metrics.
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Facilitate cross‑department collaboration on fraud‑detection models by using Perpetual ML notebooks that auto‑track experiments, optimize for business objectives like false‑positive reduction, and provide a single source of truth in the secure registry for auditability.
Who is it for?
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Data analysts
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Business analysts
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Decision makers
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Ml enthusiasts
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Product designers
