A carbon-aware ML training scheduling system that optimises machine learning workloads using real-time UK grid carbon intensity data. It simulates ML training, finds optimal low-carbon execution windows, and visualises carbon savings through an interactive multi-page Streamlit analytics dashboard.
Carbon-aware MCP scheduler for agentic AI workflows. Defer "do it later" tasks to the cleanest electricity-grid hour inside a deadline — 40–70 % lower CO2, 50 % cheaper via Batch APIs. Apache-2.0.
