# Cloud Machine Learning

- Institution: CERN
- Department: Computing Seminar
- Speaker: Joseph Sirosh
- Year: 2015
- Language: English
- Subjects: Computer science
- Source: [CERN](https://videos.cern.ch/record/3005094)
- Licence: [CERN](http://copyright.web.cern.ch)
- Who can watch: Tweens profiles and older (sources give no ratings; placed by Invisible Network from the source's own words)

Joseph Sirosh will talk about how the cloud is changing the picture for data science and machine learning. Bringing the power of the cloud to machine learning makes it possible to dramatically compress the time it takes to develop and deploy analytics in production. With examples from various case studies he will show how Azure Machine Learning, Microsoft's new product, enables the use of the latest algorithms for big data, and how easily scalable cloud APIs can be built and deployed to enable intelligence in any connected application.

About the speaker

Joseph Sirosh is the corporate vice president of the Information Management and Machine Learning (IMML) team in the Cloud and Enterprise group at Microsoft Corp. Sirosh and his IMML team have delivered several innovative advanced analytics services in the past year including Microsoft Azure Machine Learning, Azure Stream Analytics and Azure Data Factory. His team is also involved in the recent agreement by Microsoft to acquire Revolution Analytics, the leading commercial provider of software and services for R, the world's most widely used programming language for statistical computing and predictive analytics. Sirosh joined Microsoft in fall 2013 from Amazon.com Inc. where he was vice president for the Global Inventory Platform and chief technology officer of the core retail business. Before joining Amazon, he worked for Fair Isaac Corp. as vice president of research and development. He is passionate about machine learning and its applications and has been active in the field since 1990.

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## Lectures

1. [Cloud Machine Learning](https://invisiblenetwork.ai/learn/cern/3005094/06b63c0d9161), 1 h 7 min (CERN's player)
