Microsof t Azure Machine Learning
By Marshall Copeland, Julian Soh, A. Puca, Mike Manning, David Gollob. 2015
72 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Information Systems
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Reza Shokri
- Congzheng Song
- Vitaly Shmatikov
- Emmett Witchel
- Prateek Mittal
- Tyler Hunt
- Liwei Song
- Ivan Marsic
In Information Systems
- Congzheng Song
- Vitaly Shmatikov
- Liang Luo
- Jacob Nelson
- Arvind Krishnamurthy
- Ming Liu
- Kishore Atreya
- Emmett Witchel
Citations in the corpus
- CryptoDL: Deep Neural Networks over Encrypted Data (2017, background): “In a nutshell, this is Machine Learning as a Service (MLaaS), and several such services are currently offered including Microsoft Azure Machine Learning [32], Google Prediction API [33], GraphLab [34], and Ersatz Labs [35].”
- IncBricks: Toward In-Network Computation with an In-Network Cache (2017, background): “Modern applications such as big data analytics and distributed machine learning workloads [7, 11, 18, 31, 49] generate a tremendous amount of network traffic.”
- A Survey on Distributed Machine Learning (2019, methodology): “Microsoft Azure Machine Learning allows model deployment through Azure Kubernetes, through a batch service, or by using CNTK VMs [102].”
- Deep Learning for RFID-Based Activity Recognition (2016, methodology): “The network was developed with a Microsoft Azure cloud computing platform [8] and locally with Google TensorFlow [9].”
- Chiron: Privacy-preserving Machine Learning as a Service (2018, background): “Many major cloud providers now offer this service, including Google’s Prediction API [4] (soon to be replaced by Cloud Machine Learning Engine), Amazon ML [2], and Microsoft’s Azure ML [13].”