Responsible AI in Practice with Sarah Bird

1024 1024 The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

Today we continue our Azure ML at Microsoft Ignite series joined by Sarah Bird, Principal Program Manager at Microsoft.

Sarah, whose work lies primarily in machine learning systems, is now focused on bringing machine learning research into production through Azure ML, including responsible AI. At Ignite, Microsoft released a handful of new tools focused on responsible machine learning, including the Azure Machine Learning ‘Machine Learning Interpretability Toolkit’. In our conversation, Sarah walks us through this toolkit, detailing use cases and the experience users can expect when using it. We also discuss the idea of changing “Black-Box” models to “Glass-Box Models”, Sarah’s recent work in differential privacy, including risks and benefits, and her work in the broader ML community, including being a founding member of the MLSys conference.

Thanks to our sponsor!

I’d like to thank Microsoft for their support of the show, and their sponsorship of this series. Thanks to decades of breakthrough research and technology, Microsoft is making AI real for businesses with Azure AI, a set of services that span vision, speech, language processing, custom machine learning, and more. Millions of developers and data scientists around the world are using Azure AI to build innovative applications and machine learning models for their organizations, including 85% of the Fortune 100. Microsoft customers like Spotify, Lexmark and Airbus choose Azure AI because of its proven enterprise-grade capabilities and innovations, wide range of developer tools and services, and trusted approach. Stay tuned to learn how Microsoft is enabling developers, data scientists, and MLOps and DevOps professionals across all skill levels to increase productivity, operationalize models at scale and innovate faster and more responsibly with Azure Machine Learning.

Learn more here!

About Sarah

Mentioned in the Interview

  • Presentation: Responsible AI: Building trustworthy, secure and transparent machine learning
  • Azure Machine Learning ‘Machine Learning Interpretability Toolkit’
  • ONNX
  • Pytorch
  • SHAP
  • LIME
  • Article: Microsoft and Harvard to develop privacy platform.
  • TWIML Presents: Differential Privacy
  • ICML 2019 Invited Talk: The U.S. Census Bureau Tries to be a Good Data Steward in the 21st Century
  • Third Conference on Machine Learning and Systems (MLSys)
  • Systems for ML Workshop (NeurIPS 2019)
  • Check it out

    “More On That Later” by Lee Rosevere licensed under CC By 4.0

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