Autoscheduling enables higher performance end to end model optimization from TVM, while also enabling users to write custom operators even easier than before.

Today, machine learning engineers and data scientists use popular frameworks such as Scikit-learn, XGBoost, and LightGBM to train and deploy classical ML models such as linear and logistic regression, decision trees and gradient boosting.

As AI models get larger, the importance of each weight for a typical inferencing decreases...

The natural language processing (NLP) community has been transformed by the recent performance and versatility of transformer models from the deep learning research community...