Sep 23–26, 2019

Schedule: Culture and Organization sessions

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9:00am12:30pm Tuesday, September 24, 2019
Location: 1A 10
Rossella Blatt Vital (Wonderlic)
Creating and leading a successful ML strategy is an elegant orchestration of many components: master the key ML concepts, operationalize the ML workflow, prioritize highest value projects, build a high performing team, nurture strategic partnerships, align with the company’s mission, etc. This tutorial aims to share insights and lessons learned in how to create and lead a flourishing ML practice. Read more.
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9:00am12:30pm Tuesday, September 24, 2019
Location: 1A 12/14
Sourav Dey (Manifold), Jakov Kucan (Manifold)
In this tutorial, we will walk through the six steps of our Lean AI process and explain how they help your ML engineers work as an an integrated part of your development and production teams. We will also walk through a hands-on example using real-world data from one of our client companies, so you can get up and running with Docker and Orbyter and see first-hand how streamlined they can make... Read more.
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1:30pm5:00pm Tuesday, September 24, 2019
Location: 1A 10
Mac Steele (Domino Data Lab), Nick Elprin (Domino Data Lab)
The honeymoon era of data science is ending, and accountability is coming. Not content to wait for results that may or may not arrive, successful data science leaders must deliver measurable impact on an increasing share of an enterprise’s KPIs. Attendees will learn how leading organizations take a holistic approach to people, process, and technology to build a sustainable competitive advantage. Read more.
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1:30pm5:00pm Tuesday, September 24, 2019
Location: 1E 11
Ted Malaska (Capital One), Jonathan Seidman (Cloudera)
The enterprise data management space has changed dramatically in recent years, and this had led to new challenges for organizations in creating successful data practices. In this presentation we’ll provide guidance and best practices from planning to implementation based on years of experience working with companies to deliver successful data projects. Read more.
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11:20am12:00pm Wednesday, September 25, 2019
Location: 1A 08/10
Ann Spencer (Domino Data Lab), Paco Nathan (Derwen, Inc.), Amy Heineike (Primer), Pete Warden (TensorFlow)
Are you a data scientist that has wondered "why does it take so long to deploy my model into production?" Are you an engineer that has ever thought "data scientists have no idea what they want"? You are not alone. Join us for a lively discussion panel, with industry veterans, to chat about best practices and insights regarding how to increase collaboration when developing and deploying models. Read more.
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11:20am12:00pm Wednesday, September 25, 2019
Location: 1E 12/13
Brian Dalessandro (SparkBeyond)
While Data Science value is well recognized within Tech, our experience with leaders across industries shows that the ability to realize and measure business impact is not universal. A core issue is DS programs face unique risks that many leaders aren’t trained to hedge against. This talk addresses these risks and advocates for new ways to think about and manage data science programs. Read more.
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1:15pm1:55pm Wednesday, September 25, 2019
Location: 1E 10/11
Michael Stonebraker (Tamr, Inc.)
As a steward for your enterprise’s data and digital transformation initiatives, you’re tasked with making the right choice. But before you can make those decisions, it’s important to understand what NOT to do when planning for your organization’s Big Data initiatives. Dr Michael Stonebraker, Adjunct Professor, MIT, & Co-Founder/CTO, Tamr will discuss his Top 10 Big Data Blunders. Read more.
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5:25pm6:05pm Wednesday, September 25, 2019
Location: 3B - Expo Hall
Sireesha Muppala (Amazon Web Services), Shelbee Eigenbrode (Amazon Web Services), Emily Webber (Amazon Web Services)
Mansplaining. Know it? Hate it? Want to make it go away? In this session we tackle the chronic problem of men talking over or down to women and its negative impact on career progression for women. We will also demonstrate an Alexa skill that uses deep learning techniques on incoming audio feeds. We discuss ownership of the problem for both women and men, and suggest helpful strategies. Read more.
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11:20am12:00pm Thursday, September 26, 2019
Location: 1E 10/11
Gayle Bieler (RTI International)
This presentation is about building a thriving Center for Data Science within a large and well-respected non-profit research institute. I'll discuss my transformation from an entrepreneurial statistician to data science leader, as well as some of our most impactful projects and best adventures to date--solving important national problems, improving our local communities, and transforming research. Read more.
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11:20am12:00pm Thursday, September 26, 2019
Location: 3B - Expo Hall
Brian Keng (Rubikloud Technologies Inc)
Automating decisions require a system to consider more than just a data-driven prediction. Real-world decisions require additional constraints and fuzzy objectives to ensure that they are robust and consistent with business goals. This talk will describe how to leverage modern machine learning methods and traditional mathematical optimization techniques for decision automation. Read more.
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1:15pm1:55pm Thursday, September 26, 2019
Location: 1E 10/11
David Castillo (Capital One)
The head of Capital One's Center for Machine Learning will share best practices for building a Responsible AI program in the enterprise, from multidisciplinary internal working groups to research & development. Read more.
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1:15pm1:55pm Thursday, September 26, 2019
Location: 1E 12/13
Usama Fayyad (Open Insights & OODA Health, Inc.), Hamit Hamutcu (Analytics Center)
Ever confused about what it takes to be a data scientist? Or curious about how companies recruit, train and manage analytics resources? This presentation covers insight from the most comprehensive research effort to-date on the data analytics profession, propose a framework for standardization of roles in the industry and methods for assessing skills. Read more.
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3:45pm4:25pm Thursday, September 26, 2019
Location: 1E 10/11
Jonathan Tudor (GE Aviation), Ross Schalmo (GE Aviation)
GE Aviation has made it a mission to implement Self-Service Data. To ensure success beyond initial implementation of tools, the Data Engineering and Analytics teams at GE Aviation created initiatives designed to foster engagement from an ongoing partnership with each part of the business to the gamification of tagging data in a data catalog to forming a Published Dataset Council. Read more.
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4:35pm5:15pm Thursday, September 26, 2019
Location: 1E 07/08
Evgeny Vinogradov (Yandex.Money)
With a microservice architecture, DWH is a first place where all the data gets together. It supplied by many different datasources. It is used for many purposes – from near-OLTP till models fitting and realtime classifying. Talk will cover our experience in management and scaling of data Engineering Team and infrastructure for support of 20+ Product Teams. Read more.

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