Built with Rust, Δ is designed for high performance, making it ideal for compute-intensive machine learning tasks.

APIs are designed for simplicity, making it easy for beginners to get started while providing advanced customization options for experienced users.

The framework is modular, allowing users to plug in custom layers, optimizers, or preprocessing pipelines tailored to their unique needs.

It provides highly efficient and scalable tools for building and training neural networks, supporting both small-scale experiments and large-scale production systems.

Includes support for classical ML algorithms such as decision trees, random forests, SVMs and more.

Native support for distributed and parallel training ensures that Delta scales effortlessly across multi-core systems and cloud environments.

Direct access to datasets and models managed by the Nebula registry, public or private.

Manage datasets and models directly from a powerful CLI, providing full control over your workflow without leaving the terminal.

Run multiple ML projects on the same machine without conflicts, ensuring that dependencies are isolated for seamless development.

Organize datasets efficiently by metadata, versions, variants, dependencies, and lifecycles, enabling easy tracking and reproducibility.

Access and manage pretrained models with versioning and adaptations, enabling easy integration into your projects and reducing time spent on training.

Use prebuilt templates based on the Delta framework for faster setup, allowing you to quickly begin experiments with minimal configuration.

Browse datasets and models shared by the community in the Nebula registry, ensuring access to high-quality resources for your projects.

Host your own Nebula registry for secure and confidential work, keeping sensitive data and models private while maintaining efficient access management.