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Perpetual ML

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100x faster ML training without hyperparameter tuning

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Perpetual ML
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About Perpetual ML

Perpetual ML accelerates model training by more than 100x through the elimination of the time-consuming hyperparameter optimization step. It empowers businesses with Perpetual ML Suite which is a 100x faster, scalable, explainable, end-to-end, all-in-one, low-code / no-code native app for modern data warehouses. It offers features like PerpetualBooster for faster initial training, continual learning, better confidence intervals with Conformal Prediction, geographic data learning, model monitoring, and suitability for various ML tasks. It is currently being developed for Snowflake and will be available for Databricks and other data warehouses later. It eliminates the need for specialized hardware like GPU or TPU.

How to Use

Contact Perpetual ML for a free trial to see it in action. The suite is designed as a low-code/no-code native app for modern data warehouses, allowing users to unlock insights and actions from their data quickly.

Perpetual ML's

Key Features

  • 100x faster initial training with PerpetualBooster
  • Continual learning without starting from scratch
  • Better confidence intervals with Conformal Prediction
  • Geographic data learning
  • Model monitoring
  • Suitable for tabular classification, regression, time series, learning to rank, and text classification
  • Portable across different data warehouses
  • Effortless parallelism
  • No specialized hardware required

Use Cases

  • Accelerating model training in data warehouses
  • Improving the speed and efficiency of machine learning workflows
  • Monitoring models and detecting distribution shift
  • Building and deploying machine learning models without specialized hardware

Key Features

100x faster initial training with PerpetualBooster
Continual learning without starting from scratch
Better confidence intervals with Conformal Prediction
Geographic data learning
Model monitoring
Suitable for tabular classification, regression, time series, learning to rank, and text classification
Portable across different data warehouses
Effortless parallelism
No specialized hardware required

Pros & Cons

Pros
  • 100x faster training by removing hyperparameter optimization
  • No need for GPU/TPU, reducing hardware costs
  • Supports continual learning – updates models incrementally
  • Built-in model monitoring and drift detection
  • Low-code/no-code interface suitable for non-experts
  • Provides explainable predictions
  • Integrates directly within Snowflake for seamless workflows
Cons
  • Currently only available for Snowflake; other platforms (Databricks, etc.) are pending
  • Pricing requires contact, no transparent pricing available
  • Limited to supported ML tasks (tabular, time series, text classification, learning-to-rank)
  • May not suit deep learning or image/audio tasks

Best For

Accelerating model training in data warehousesImproving the speed and efficiency of machine learning workflowsMonitoring models and detecting distribution shiftBuilding and deploying machine learning models without specialized hardware

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FAQ

What is Perpetual ML and how does it achieve 100x speedup?
Perpetual ML eliminates the time-consuming hyperparameter optimization step, which often requires dozens of training runs. Its PerpetualBooster algorithm directly learns optimal parameters during a single training pass, resulting in initial training speeds over 100x faster than traditional gradient boosting.
Which data warehouses does Perpetual ML support?
Perpetual ML Suite is currently developed for Snowflake. Support for Databricks and other data warehouses is planned for future releases.
Do I need specialized hardware like GPU to run Perpetual ML?
No. Perpetual ML is designed to run efficiently on standard CPU hardware, eliminating the need for expensive GPUs or TPUs.
What types of machine learning tasks can Perpetual ML handle?
It supports tabular classification, regression, time series forecasting, learning to rank, and text classification.