Perpetual ML

Perpetual ML Suite is a machine learning platform designed for solo developers and data science teams who need to build, train, deploy, and monitor models within a single web interface that integrates directly with existing data warehouses. The platform aims to reduce the time from raw data to production predictions by automating repetitive tasks and providing continuous learning capabilities. At the core of Perpetual ML is the Auto Train feature, which uses a proprietary algorithm called PerpetualBooster. This algorithm has been ranked first on public AutoML benchmarks, meaning it can automatically search for the best model architectures, hyperparameters, and feature engineering steps without manual intervention. Users simply point the system to their dataset, and the platform handles data preprocessing, model selection, cross-validation, and ensemble creation. This allows teams with limited machine learning expertise to achieve competitive predictive performance quickly. Beyond one-time training, Perpetual ML supports continual learning. Models can be incrementally updated as new data streams in, significantly reducing total retraining time compared to full retraining from scratch. The system automatically detects when model performance degrades due to concept drift or data drift and triggers retraining cycles. This is critical for applications like fraud detection, recommendation systems, or demand forecasting where patterns change over time. Users can schedule retraining frequencies or let the platform decide based on monitored metrics. For experiment tracking, Perpetual ML provides a centralized dashboard where all model runs, hyperparameters, evaluation metrics, and artifacts are logged automatically. Teams can compare different versions side by side, filter by da