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

Dynamic resources changes for multi-dimensional parallelism training

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

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