Azure

Microsoft Azure Machine Learning Studio. Microsoft Azure is an ever-expanding set of cloud services to help your organization meet your business challenges. It’s the freedom to build, manage, and deploy applications on a massive, global network using your favorite tools and frameworks.

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References in zbMATH (referenced in 31 articles )

Showing results 1 to 20 of 31.
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  1. Bülbül, Kerem; Noyan, Nilay; Erol, Hazal: Multi-stage stochastic programming models for provisioning cloud computing resources (2021)
  2. Chamberlain, Jonathan; Simhon, Eran; Starobinski, David: Preemptible queues with advance reservations: strategic behavior and revenue management (2021)
  3. Krupa, Artur; Antoniuk, Izabella: Digital representation of tissues in high compute bioelectromagnetics (2021)
  4. Vasantam, Thirupathaiah; Mazumdar, Ravi R.: Sensitivity of mean-field fluctuations in Erlang loss models with randomized routing (2021)
  5. Vo, Viet; Lai, Shangqi; Yuan, Xingliang; Nepal, Surya; Liu, Joseph K.: Towards efficient and strong backward private searchable encryption with secure enclaves (2021)
  6. Vovk, Vladimir: Testing randomness online (2021)
  7. Baranowski, Mikołaj; Belloum, Adam; Cushing, Reginald; Valkering, Onno: Cookery: a framework for creating data processing pipeline using online services (2020)
  8. Saxena, Apoorv; Claeys, Dieter; Zhang, Bo; Walraevens, Joris: Cloud data storage: a queueing model with thresholds (2020)
  9. Gao, Jiayang; Iyer, Krishnamurthy; Topaloglu, Huseyin: When fixed price meets priority auctions: competing firms with different pricing and service rules (2019)
  10. Naji Dmeiri, David A. Tomassi, Yichen Wang, Antara Bhowmick, Yen-Chuan Liu, Premkumar Devanbu, Bogdan Vasilescu, Cindy Rubio-González: BugSwarm: Mining and Continuously Growing a Dataset of Reproducible Failures and Fixes (2019) arXiv
  11. Roy, Asim; Qureshi, Shiban; Pande, Kartikeya; Nair, Divitha; Gairola, Kartik; Jain, Pooja; Singh, Suraj; Sharma, Kirti; Jagadale, Akshay; Lin, Yi-Yang; Sharma, Shashank; Gotety, Ramya; Zhang, Yuexin; Tang, Ji; Mehta, Tejas; Sindhanuru, Hemanth; Okafor, Nonso; Das, Santak; Gopal, Chidambara N.; Rudraraju, Srinivasa B.; Kakarlapudi, Avinash V.: Performance comparison of machine learning platforms (2019)
  12. Wang, Liang; Wang, Baocang; Song, Wei; Zhang, Zhili: A key-sharing based secure deduplication scheme in cloud storage (2019)
  13. Alexander Foss; Marianthi Markatou: kamila: Clustering Mixed-Type Data in R and Hadoop (2018) not zbMATH
  14. Convolbo, Moïse W.; Chou, Jerry; Hsu, Ching-Hsien; Chung, Yeh Ching: GEODIS: towards the optimization of data locality-aware job scheduling in geo-distributed data centers (2018)
  15. Mrozek, Dariusz: Scalable big data analytics for protein bioinformatics. Efficient computational solutions for protein structures (2018)
  16. Murray, Riley; Khuller, Samir; Chao, Megan: Scheduling distributed clusters of parallel machines : primal-dual and LP-based approximation algorithms (2018)
  17. Karthik, A.; Mukhopadhyay, Arpan; Mazumdar, Ravi R.: Choosing among heterogeneous server clouds (2017)
  18. Tianyu Gu, Brendan Dolan-Gavitt, Siddharth Garg: BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain (2017) arXiv
  19. Ábrahám, Erika; Kremer, Gereon: Satisfiability checking: theory and applications (2016)
  20. Karakoyunlu, Cengiz; Chandy, John A.: Exploiting user metadata for energy-aware node allocation in a cloud storage system (2016)

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