- The Case for Learning-and-System Co-design [Paper]
- Mike Liang, C.J., Xue, H., Yang, M. and Zhou, L., 2019.
- ACM SIGOPS Operating Systems Review, 53(1), pp.68-74.
- Summary: Make the system learnable. Propose a framework named AutoSys which contains both training plane and inference plane
- AI infrastructures list [GitHub]
- cortexlabs/cortex: Deploy machine learning applications without worrying about setting up infrastructure, managing dependencies, or orchestrating data pipelines. [GitHub]
- Osquery is a SQL powered operating system instrumentation, monitoring, and analytics framework. [Facebook Project]
- Seldon: Sheldon Core is an open source platform for deploying machine learning models on a Kubernetes cluster.[GitHub]
- Kubeflow: Kubeflow is a machine learning (ML) toolkit that is dedicated to making deployments of ML workflows on Kubernetes simple, portable, and scalable. [GitHub]
- Polytaxon: A platform for reproducible and scalable machine learning and deep learning on kubernetes. [GitHub]
- MLOps on Azure [GitHub]
- Flame: An ML framework to accelerate research and its path to production. [GitHub]
- Ludwig is a toolbox built on top of TensorFlow that allows to train and test deep learning models without the need to write code. [GitHub]
- intel-analytics/analytics-zoo Distributed Tensorflow, Keras and BigDL on Apache Spark [GitHub]