An internal platform designed for Ascendeum’s ML engineers and researchers to manage every stage of the machine learning lifecycle, from data prep to deployment.

It empowers teams to build custom models or start with existing templates, tweak architecture or logic, and execute end-to-end workflows through a drag-and-drop interface.

Each step from feature engineering and data handling to tuning, evaluation, and deployment is broken into modular blocks that reduce complexity and promote clarity across the process.

Developers can dive into code at any step, making adjustments as needed while still benefiting from a structured UI that tracks and visualizes every change.

Here are some snippets.

This interface acts as a flexible workspace where developers can dive into code, adjust configurations, and fine-tune parameters as needed.