Learn how to design, develop, deploy and iterate on production-grade ML applications.

Superduper: Build end-to-end AI applications and agent workflows on your existing data infrastructure and preferred tools - without migrating your data.

Learn how to design, develop, deploy and iterate on production-grade ML applications.

deploy ML Infrastructure and MLOps tooling anywhere quickly and with best practices with a single command

Nerlnet is a framework for research and development of distributed machine learning models on IoT

A fully adaptive, zero-tuning parameter manager that enables efficient distributed machine learning training

Repository that contains the code for the paper titled, 'Unifying Distillation with Personalization in Federated Learning'.

Dynamic resources changes for multi-dimensional parallelism training

Caffe: a fast open framework for deep learning. Caffe-pslite: run deep learning in a cluster with ps-lite (including SSP model)

Akka-based framework for distributed ML on fog