Perpetual ML

Perpetual ML offers a 100x faster machine learning suite optimized for modern data warehouses, transforming data analysis and model deployment.

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About Perpetual ML

Perpetual ML significantly accelerates model training by over 100 times by eliminating lengthy hyperparameter tuning processes. It provides a comprehensive, scalable, and explainable low-code/no-code application designed for modern data warehouses. Key features include PerpetualBooster for rapid initial training, continuous learning capabilities, enhanced confidence intervals via Conformal Prediction, geographic data analysis, and robust model monitoring. Currently compatible with Snowflake and planned for Databricks and other data platforms, it eliminates the need for specialized hardware such as GPUs or TPUs, making advanced machine learning accessible and efficient.

How to Use

Request a free trial of Perpetual ML to experience its capabilities firsthand. Designed as a low-code/no-code native application, it allows users to quickly derive insights and take actions from their data within modern data warehouse environments.

Features

Supports geographic data analysis

Enables continuous learning without retraining from scratch

Compatible with classification, regression, time series, and ranking tasks

Includes comprehensive model monitoring

Easily portable across multiple data warehouse platforms

Offers effortless parallel processing

Does not require specialized hardware like GPUs or TPUs

Provides improved confidence intervals through Conformal Prediction

Achieves 100x faster initial training with PerpetualBooster

Use Cases

Speeding up machine learning workflows

Building models without specialized hardware

Accelerating training within data warehouses

Monitoring models for performance and data shifts

Best For

Organizations using modern data warehousesData scientists seeking faster model trainingData analysts analyzing large datasetsMachine learning engineers optimizing workflows

Pros

Drastically reduces model training time

Compatible across various data warehouse platforms

User-friendly low-code/no-code interface

Built-in generalization algorithms eliminate hyperparameter tuning

No need for expensive hardware investments

Provides scalable, explainable machine learning solutions

Cons

Initially available for Snowflake, with support for Databricks and others coming soon

Pricing details require direct contact with Sales

Frequently Asked Questions

Find answers to common questions about Perpetual ML

What is PerpetualBooster?

PerpetualBooster is a feature that speeds up initial model training by 100 times, utilizing a built-in generalization algorithm that removes the need for hyperparameter tuning.

Which machine learning tasks can Perpetual ML handle?

It is suitable for tabular classification, regression, time series forecasting, ranking, and text classification using embeddings.

Which data warehouses are supported by Perpetual ML?

Currently, it supports Snowflake, with plans to expand to Databricks and other major platforms soon.

Is specialized hardware required to run Perpetual ML?

No, it operates efficiently without GPUs or TPUs, making it accessible with existing hardware and software.

How does Perpetual ML improve model confidence?

It uses Conformal Prediction to provide more accurate and reliable confidence intervals for model predictions.