Sep 23–26, 2019

Schedule: Automation in data science and data sessions

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11:20am12:00pm Wednesday, September 25, 2019
Location: 1A 23
Moty Fania (Intel)
Moty Fania details Intel’s IT experience of implementing a sales AI platform. This platform is based on streaming, microservices architecture with a message bus backbone. It was designed for real-time data extraction and reasoning and handles the processing of millions of website pages and is capable of sifting through millions of tweets per day. Read more.
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2:55pm3:35pm Wednesday, September 25, 2019
Location: 1A 21
Kai Liu (BING) (Microsoft), Jack Zhang (Microsoft), Jing Zhao (Microsoft)
Facilitating large-scale deep learning projects in parallel requires effort and innovation. Bing now runs a deployment of thousands of servers to address this challenge. Kai Liu, Jack Zhang, and Jing Zhao detail how Bing provides training services, offline data processing, vector hosting, and inferencing service offline to help data scientists through all steps in the project lifecycle. Read more.
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2:05pm2:45pm Thursday, September 26, 2019
Location: 1A 12
Andrew Leamon (Comcast), Wadkar Sameer (Comcast NBCUniversal)
And overview of the Data Management and privacy challenges around automating ML model (re)deployments and stream based inferencing at scale. Read more.
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2:05pm2:45pm Thursday, September 26, 2019
Location: 1A 21
Diego Oppenheimer (Algorithmia)
Machine Learning (ML) will fundamentally change the way we build and maintain applications. How can we adapt our infrastructure, operations, staffing, and training to meet the challenges of the new Software Development Life Cycle (SDLC) without throwing away everything that already works? Read more.
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3:45pm4:25pm Thursday, September 26, 2019
Location: 1A 21
Sireesha Muppala (Amazon Web Services), Shelbee Eigenbrode (Amazon Web Services), Randall DeFauw (Amazon Web Services)
As an increasing level of automation is becoming available to data science, there is a balance between automation and quality that needs to be maintained. Applying DevOps practices to machine learning workloads not only brings models to the market faster but also maintains the quality and integrity of those models. This presentation will focus on applying DevOps practices to ML workloads. Read more.

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