Scalable AI for Good - Databricks

Scalable AI for Good

As AI becomes more ubiquitous and scalable we aim to apply these technologies to help improve the planet. This talk will explore Microsoft’s latest contributions to the Apache Spark and Machine Learning communities with a special focus on AI for environmental and social impact. In particular, we will share how to use Azure Databricks, Azure Machine Learning and Microsoft ML for Apache Spark to explore over 5,000 years of human creativity with the Metropolitan Museum of Art, and how Microsoft uses Apache Spark to help the protect endangered species.

 

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About Mark Hamilton

Microsoft

Mark is a software engineer on Microsoft’s Applied AI team and a machine learning PhD student at the MIT Computer Science and AI Lab. Mark leads Microsoft ML for Apache Spark (http://aka.ms/spark), a distributed machine learning and microservice orchestration library. He has applied this work to problems in wildlife conservation, accessibility, and art museum outreach. Mark is currently researching how information theory and abstract algebra can yield new deep learning architectures in professor William T Freeman’s lab.

About Christina Lee

Microsoft

Christina is a program manager in Microsoft’s Applied AI team focused on the Azure Cognitive Services platform. She is currently working on Microsoft ML for Apache Spark (http://aka.ms/spark), a distributed machine learning library. Christina received her Bachelor’s in Mathematics and Master’s in Computer Science from Stanford University.