Perpetual ML

Perpetual ML includes several key features:

• Auto Train: Automatically train models using PerpetualBooster, which is the #1 algorithm on AutoML benchmark.
• Continual Learning: Cut total training time from O(n^2) to O(n), where n is the number of batches.
• Optimal Business Decisioning: Directly optimize user-defined business objectives, such as maximizing profit or minimizing risk.
• Experiment Tracking: Track, compare, and reproduce all Auto Train experiments in one organized place.
• Model Registry: A secure, version-controlled repository for storing, managing, and sharing production-ready models.
• Monitoring: Monitor metrics, data drift, and model drift without retraining or ground truth.
• Deployment: Deploy models for batch and real-time inference from a unified platform.
• Marimo Notebooks: A reactive and collaborative notebook environment for data exploration and model development.

Perpetual ML is suitable for various machine learning tasks, including:

• Automated model training: Quickly train models with Auto Train, leveraging the top-ranked PerpetualBooster algorithm.
• Continual learning: Efficiently update models with new data batches, reducing training time significantly.
• Business decision optimization: Align model predictions with specific business goals, such as profit maximization or risk minimization.
• Model management: Use the model registry to version and share models across teams.
• Monitoring and deployment: Keep track of model performance and deploy models for batch or real-time inference seamlessly.
• Data exploration: Use Marimo notebooks for interactive and collaborative data analysis.

The page does not provide specific pricing details. It includes a link to a pricing page, but the content is not available in the provided text. For accurate pricing information, users should visit the Perpetual ML website's pricing section.