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

Superduper: Integrate AI models and machine learning workflows with your database to implement custom AI applications, without moving your data. Including streaming inference, scalable model hosting, training and vector search.

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'.

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