Distributed LLM pretraining during renewable curtailment windows 🌱

Learn to optimize machine learning tasks for environmental sustainability. Discover how to use real-time electricity data and low-carbon energy sources for model training and inference, reducing the carbon footprint of your cloud operations.

Hackathon winner at AI Engineer World Fair Hackathon: Transforming code, one function at a time, to reduce digital carbon footprints and create a more sustainable digital world.

End-to-end AI deployment decision system combining real model benchmarking, system-level optimization, and infrastructure-aware trade-off analysis (latency, cost, energy, carbon).

A lightweight pipeline for carbon-aware job scheduling using 72-hour forecasts of grid renewable energy share.

EcoLogic is a local Streamlit toolkit for generating and evaluating algorithmic refactors across single files or full codebases. It predicts energy use with feature-based ML, profiles Python/C++/.NET/Java workloads, delivers optimized code with SHAP-powered explainability, and creates shareable PDF certificates for auditable, interpretable results.

Pre-alpha carbon-aware DevOps / CI/CD reference toolkit.

Carbon-aware cloud workload scheduler that reduces emissions by intelligently shifting workloads across time using multi-objective optimization.

Blackout Markets is a shadow optimizer for AI infrastructure teams. It recommends when GPU workloads should run, wait, or move regions based on energy cost, carbon intensity, GPU capacity, latency, reliability, and policy constraints.

A thesis implementing and evaluating a framework for energy-aware federated learning, capable of both simulation and real-time monitoring.