Green AI and AutoML: Predicting Carbon Footprint of Machine Learning Models with Meta-Learning
Green AI · Year: 2022
CRP ML Course Project / Academic Research Paper
https://www.epfl.ch/labs/mlo/wp-content/uploads/2022/10/crpmlcourse-paper1253.pdf
Peer-reviewed green AI research and applied efficiency-focused machine learning studies powered by structured experimentation, automated model benchmarking, and reproducible AutoML pipelines with MLJAR.
Explore peer-reviewed and applied machine learning studies built on structured experimentation and reproducible pipelines with MLJAR.
A private, AI-powered Python notebook designed for reproducible machine learning experiments, structured benchmarking, and applied research workflows.
Design structured pipelines, save experiment runs, and compare results across iterations with full transparency.
Run all workflows directly on your machine and keep sensitive datasets private without mandatory cloud uploads.
Automatically compare candidate models, perform cross-validation, and run hyperparameter optimization with full metric visibility.
Explore peer-reviewed and applied machine learning studies across diverse domains, including healthcare analytics, financial modeling, manufacturing optimization, and structured data classification problems.
Run automated model benchmarking, hyperparameter optimization, and autonomous experiments while keeping full control over your data.