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 23/24
Secondary topics:  Data, Analytics, and AI Architecture
Moty Fania (Intel)
In this session, Moty Fania will share Intel’s IT experience of implementing a Sales AI platform. This platform is based on streaming, micro-services architecture with a message bus backbone. It was designed for real-time, data extraction and reasoning. The platform handles processing of millions of website pages and capable of sifting thru millions of tweets per day. Read more.
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2:55pm3:35pm Wednesday, September 25, 2019
Location: 1A 21/22
Secondary topics:  Data, Analytics, and AI Architecture, Deep Learning
Kai Liu (Microsoft (BING))
Facilitating large scale of deep learning projects in parallel requires some effort and innovation. Bing is now running a deployment of thousands of servers to address this challenge. We provides training services, offline data processing, vector hosting, and inferencing service at offline fashion to help data scientists through all steps in the project life cycle. Read more.
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2:05pm2:45pm Thursday, September 26, 2019
Location: 1A 12/14
Secondary topics:  Data quality, data governance and data lineage, Media and Advertising, Model Development, Governance, Operations
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 21/22
Secondary topics:  Model Development, Governance, Operations
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 21/22
Secondary topics:  Cloud Platforms and SaaS, Deep dive into specific tools, platforms, or frameworks, Model Development, Governance, Operations
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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