Presented By O’Reilly and Cloudera
Make Data Work
September 11, 2018: Training & Tutorials
September 12–13, 2018: Keynotes & Sessions
New York, NY

Schedule: Machine Learning in the enterprise sessions

Machine learning has enormous potential but it’s important to identify appropriate problems and use cases that one can start with. The key to using any new set of tools and technologies is to understand what they can and cannot do. How do you put your organization in a position to take advantage of ML technologies? Because ML has the potential to affect every aspect of an organization, we are highlighting several companies who have invested resources in training and organizing their workforce on these new technologies.

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9:00am–12:30pm Tuesday, 09/11/2018
Location: 1A 12/14 Level: Non-technical
Joshua Poduska (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 deliver measurable impact on an increasing share of an enterprise’s KPIs. Joshua Poduska details how leading organizations have taken a holistic approach to people, process, and technology to build a sustainable competitive advantage Read more.
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9:00am–5:00pm Tuesday, 09/11/2018
Location: 1E 10
Alistair Croll (Solve For Interesting), Katharina Warzel (EveryMundo), Mike Berger (Mount Sinai Health System), Sam Helmich (Deere & Company), Stephanie Fischer (datanizing GmbH), Maryam Jahanshahi (TapRecruit), Greg Quist (SmartCover Systems), Ann Nguyen (Whole Whale), Abhimanyu Verma (Novartis), Steve Otto (Navistar), Jennifer Lim (Cerner), Anand S (Gramener)
Hear practical insights from household brands and global companies: the challenges they tackled, approaches they took, and the benefits—and drawbacks—of their solutions. Read more.
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1:30pm–5:00pm Tuesday, 09/11/2018
Location: 1E 15/16 Level: Beginner
This tutorial is a primer on crafting well-conceived data science projects on course toward uncovering valuable business insights. Using case studies and hands-on skills development, we will teach techniques that are essential for a variety of audiences invested in effecting real business change. Read more.
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9:35am–9:50am Wednesday, 09/12/2018
Location: 3E
Jeffrey Wecker (Goldman Sachs)
Keynote with Jeffrey Wecker Read more.
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11:20am–12:00pm Wednesday, 09/12/2018
Location: 1E 10/11 Level: Non-technical
JF Gagne (Element AI)
The CIO is going to need a broader mandate in the company to better align their AI training and outcomes with business goals and compliance. This mandate should include an AI Governance team that is well staffed and deeply established in the company in order to catch biases that can develop from faulty goals or flawed data Read more.
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1:15pm–1:55pm Wednesday, 09/12/2018
Location: 1E 10/11 Level: Non-technical
Erin Coffman (Airbnb)
Airbnb has open-sourced many high-leverage data tools: Airflow, Superset, and the Knowledge Repo. However, adoption of these tools across Airbnb was relatively low. To make data more accessible and utilized in decision-making, Airbnb launched Data University in early 2017. Since the launch, over a quarter of the company has participated in the program, and data tool utilization rates have doubled. Read more.
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1:15pm–1:55pm Wednesday, 09/12/2018
Location: 1E 14 Level: Non-technical
Tony Baer (Ovum), Florian Douetteau (DATAIKU)
Tony Baer and Florian Douetteau share the results of research cosponsored by Ovum and Dataiku that surveyed a specially selected sample of chief data officers and data scientists on how to map roles and processes to make success with AI in the business repeatable. Read more.
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2:05pm–2:45pm Wednesday, 09/12/2018
Location: 1E 14 Level: Intermediate
David Talby (Pacific AI)
Machine learning and data science systems often fail in production in unexpected ways. David Talby shares real-world case studies showing why this happens and explains what you can do about it, covering best practices and lessons learned from a decade of experience building and operating such systems at Fortune 500 companies across several industries. Read more.
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2:55pm–3:35pm Wednesday, 09/12/2018
Location: 1E 14 Level: Intermediate
Ted Malaska (Blizzard Entertainment), Jonathan Seidman (Cloudera)
Creating a successful big data practice in your organization presents new challenges in managing projects and teams. In this session we'll provide guidance and best practices to help technical leaders deliver successful projects from planning to implementation. Read more.
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4:35pm–5:15pm Wednesday, 09/12/2018
Location: 1E 12/13 Level: Non-technical
Kimberly Nevala (SAS Institute)
Too often, the discussion of AI and ML includes an expectation—if not a requirement—for infallibility. But as we know, this expectation is not realistic. So what’s a company to do? While risk can’t be eliminated, it can be rationalized. Kimberly Nevala demonstrates how an unflinching risk assessment enables AI/ML adoption and deployment. Read more.
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4:35pm–5:15pm Wednesday, 09/12/2018
Location: 1E 14 Level: Non-technical
Cassie Kozyrkov (Google)
Many organizations aren’t aware that they have a blindspot with respect to their lack of data effectiveness, and hiring experts doesn’t seem to help. Cassie Kozyrkov examines what it takes to build a truly data-driven organizational culture and highlights a vital yet often neglected job function: the data science manager. Read more.
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5:25pm–6:05pm Wednesday, 09/12/2018
Location: Expo Hall
Mike Tung (Diffbot)
Mike Tung offers an overview of available open source and commercial knowledge graphs and explains how consumer and business applications are already taking advantage of them to provide intelligent experiences and enhanced business efficiency. Mike then discusses whats coming in the future. Read more.
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11:20am–12:00pm Thursday, 09/13/2018
Location: 1E 10/11 Level: Non-technical
Data scientists are hard to hire. But too often, companies struggle to find the right talent only to make avoidable mistakes that cause their best data scientists to leave. From org structure and leadership to tooling and infrastructure to continuing education, this talk will offer concrete (and inexpensive) tips for keeping your data scientists engaged, productive, and adding business value. Read more.
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11:20am–12:00pm Thursday, 09/13/2018
Location: 1E 14 Level: Intermediate
Mikio Braun (Zalando SE)
In order to become "AI ready", an organization not just has to provide the right technical infrastructure for data collection and processing, but also learn new skills. In this talk I will highlight three such missing pieces: making the connection between business problems and AI technology, AI driven development, and how to run AI based projects. Read more.
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1:10pm–1:50pm Thursday, 09/13/2018
Location: 1E 14 Level: Non-technical
Brandy Freitas (Pitney Bowes)
Data science is an approachable field given the right framing. Often, though, practitioners and executives are describing opportunities using completely different languages. In this session, Harvard Biophysicist-turned-Data Scientist, Brandy Freitas, will work with participants to develop context and vocabulary around data science topics to help build a culture of data within their organization. Read more.
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3:30pm–4:10pm Thursday, 09/13/2018
Location: 1E 14 Level: Intermediate
Jennifer Prendki (Figure Eight)
Agile methodologies have been widely successful for software engineering teams but seem inappropriate for data science teams, because data science is part engineering, part research. Jennifer Prendki demonstrates how, with a minimum amount of tweaking, data science managers can adapt Agile techniques and establish best practices to make their teams more efficient. Read more.
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4:20pm–5:00pm Thursday, 09/13/2018
Location: 1E 12/13 Level: Beginner
Francesca Lazzeri (Microsoft), Jaya Mathew (Microsoft)
What profession did Harvard Business Review call the Sexiest Job of the 21st Century? With the growing buzz of data science, several professionals have approached us at various events to learn more about how to become a data scientist. This session aims at raising awareness of what it takes to become a data-scientist and how artificial intelligence solutions have started to reinvent businesses. Read more.
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4:20pm–5:00pm Thursday, 09/13/2018
Location: 1A 12/14 Level: Non-technical
Brian O'Neill (Designing for Analytics)
Gartner says 85%+ of big data projects will fail, despite the fact your company may have invested millions on engineering implementation. Why are customers and employees not engaging with these products and services? CDOs, CIOs, product managers, and analytics leaders with a "people first, technology second" mission–a design strategy–will realize the best UX and business outcomes possible. #design Read more.
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4:20pm–5:00pm Thursday, 09/13/2018
Location: 1A 06/07 Level: Non-technical
Bill Franks (International Institute For Analytics)
Drawing on a recent study of the analytics maturity level of large enterprises by the International Institute for Analytics, Bill Franks discusses how maturity varies by industry, shares key steps organizations can take to move up the maturity scale, and explains how the research correlates analytics maturity with a wide range of success metrics, including financial and reputational measures. Read more.