Mar 15–18, 2020

Schedule: AI Engineering sessions

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11:00am11:40am Wednesday, March 18, 2020
Location: 210 B
Colin Spikes (Algorithmia)
ML is advancing rapidly, but only a few contributors focus on the infrastructure and scaling challenges that come with it. Colin Spikes explores why ML is a natural fit for serverless computing, a general architecture for scalable ML, and common issues when implementing on-demand scaling over GPU clusters. He provides general solutions and describes a vision for the future of cloud-based ML. Read more.
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11:50am12:30pm Wednesday, March 18, 2020
Location: 210 B
Nick Pinckernell (Comcast)
With model serving becoming easier thanks to tools like Kubeflow, the focus is shifting to feature engineering. Nick Pinckernell reviews five ways to get your raw data into engineered features (and eventually to your model) with open source tools, flexible components, and various architectures. Read more.
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1:45pm2:25pm Wednesday, March 18, 2020
Location: 210 B
Fidan Boylu Uz (Microsoft), Mario Bourgoin (Microsoft), Gheorghe Iordanescu (Microsoft)
Hyperparameter optimization for machine leaning is complex, requires advanced optimization techniques, and can be implemented as a generic framework decoupled from specific details of algorithms. Fidan Boylu Uz, Mario Bourgoin, and George Iordanescu apply such a framework to tasks like object detection and text matching in a transparent, scalable, and easy-to-manage way in a cloud service. Read more.
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4:15pm4:55pm Wednesday, March 18, 2020
Location: 210 B
Roshan Satish (DocuSign), Michael Chertushkin (John Snow Labs)
Roshan Satish and Michael Chertushkin lead you through a real-world case study about applying state-of-the-art deep learning techniques to a pipeline that combines computer vision (CV), optical character recognition (OCR), and natural language processing (NLP) at DocuSign. You'll discover how the project delivered on its extreme interpretability, scalability, and compliance requirements. Read more.

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