Presented By O'Reilly and Cloudera
Make Data Work
Feb 17–20, 2015 • San Jose, CA
Joseph Sirosh

Joseph Sirosh
CVP, Information Management and Machine Learning group, Microsoft

@josephsirosh

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 shipped public preview versions of Microsoft Azure Machine Learning, Azure Stream Analytics and Azure Data Factory. His team is also involved with the agreement to acquire Revolution Analytics, the leading provider of software and services for the R programming language.

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. In this role he had responsibility for the science and software behind Amazon’s supply chain and order fulfillment systems, as well as the central Machine Learning group, which he built and led. During his nine years at Amazon, he managed a variety of teams including forecasting, inventory, supply chain and fulfillment, fraud prevention systems, data warehouse, and a novel data-driven seller lending 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.

Sessions

9:40am–9:50am Friday, 02/20/2015
Keynotes, Sponsored
Location: Grand Ballroom 220
Joseph Sirosh (Microsoft)
Average rating: ****.
(4.67, 33 ratings)
Join Microsoft’s Joseph Sirosh for a surprising conversation about a farmer's dilemma, a professor's ingenuity and how cloud, data and devices came together to fundamentally re-imagine an age old way of doing business. Read more.
10:40am–11:20am Friday, 02/20/2015
Sponsored
Location: 210 D/H
Joseph Sirosh (Microsoft)
Average rating: ****.
(4.60, 5 ratings)
Armed with just a browser, data scientists can develop sophisticated machine learning models, and deploy them in a few clicks in cloud-hosted APIs that can be called from any device. The APIs scale elastically to power high volume intelligent apps for phones, websites and the internet of things. . . Read more.