Deploying Machine Learning (ML) algorithms within databases is a challenge due to the varied computational footprints of modern ML algorithms and the myriad of database technologies. This tutorial will explore the Azure Cognitive Services for Big Data: a framework that makes it easy to apply state-of-the-art intelligent models to enterprise data, independent of scale or datastore. We will show how to embed algorithms like OCR, Speech Recognition, Object Detection, and Sentiment Analysis into a variety of databases.  We will also dive deep into the world of planet-scale intelligent services and our recent work to unify distributed computing with microservice orchestration. Finally, we will apply these techniques to demonstrate how to create a custom visual search engine for over 5000 years of human culture.

Instructor's Bio

Mark Hamilton

 Research Development Engineer at Microsoft

Mark Hamilton is a Ph.D. student in William T Freeman's lab at the MIT Computer Science & Artificial Intelligence Laboratory and a Research Engineer on the Azure Cognitive Services Research team. At Microsoft he leads Microsoft ML for Apache Spark, an elastically distributed ML and microservice framework. His research interests include information theory, computer vision, and distributed systems. Mark values working on projects with positive social, cultural, and environmental applications.

Anand Raman Ph.D.

Director PM, Azure Cognitive Services Platform at Microsoft

Anand currently leads the product management for Azure Cognitive Services Platform. Previously he was the Chief of Staff for Microsoft Azure Data Group covering Data Platforms and Machine Learning. In the last decade, he ran the product management and the development teams at Azure Data Services, Visual Studio and Windows Server User Experience teams at Microsoft. Anand holds a Ph.D. in Computational fluid mechanics and worked several years as a researcher before joining Microsoft.

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