The Problem

Data scientists waste 30% of their time managing infrastructure instead of training models. Existing tools are fragmented and lack real-time visibility into model performance.

The Solution

AI Nexus is a unified command center for ML ops. It abstracts away the complexity of Kubernetes and GPU provisioning, allowing teams to deploy models with a single click.

Key Capabilities

• Drift Detection: Automated alerts when model accuracy degrades.
• Resource Optimization: Dynamic scaling of GPU nodes based on inference load.
• Explainability: Integrated SHAP values to explain model predictions.

Interface Design

The dashboard uses a modular grid system that allows data scientists to customize their workspace. We implemented WebSockets to stream training metrics (loss, accuracy, epoch time) in real-time without polling, ensuring the UI always reflects the true state of the cluster.

Technical Architecture

Built on FastAPI for high-performance inference and React for the frontend, utilizing WebSockets for real-time training metrics streaming.

Impact

Reduced model deployment time from 2 days to 15 minutes for a pilot enterprise client.