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.

9:00am–12:30pm Tuesday, 09/11/2018
Location: 1A 12/14 Level: Non-technical
Joshua Poduska (Domino Data Lab), Patrick Harrison (S&P Global)
Average rating: ****.
(4.29, 7 ratings)
The honeymoon era of data science is ending, and accountability is coming. Successful data science leaders deliver measurable impact on an increasing share of an enterprise’s KPIs. Joshua Poduska and Patrick Harrison detail how leading organizations have taken a holistic approach to people, process, and technology to build a sustainable competitive advantage Read more.
9:00am–5:00pm Tuesday, 09/11/2018
Location: 1E 10
Paco Nathan (derwen.ai), 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), Steve Otto (Navistar), Jennifer Lim (Cerner), Anand S (Gramener), Ian Brooks (Hortonworks)
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.
1:30pm–5:00pm Tuesday, 09/11/2018
Location: 1E 15/16 Level: Beginner
Average rating: **...
(2.67, 9 ratings)
Janet Forbes, Danielle Leighton, and Lindsay Brin lead a primer on crafting well-conceived data science projects that uncover valuable business insights. Using case studies and hands-on skills development, Janet, Danielle, and Lindsay walk you through essential techniques for effecting real business change. Read more.
9:35am–9:50am Wednesday, 09/12/2018
Location: 3E
Jeffrey Wecker (Goldman Sachs)
Average rating: ***..
(3.12, 26 ratings)
Jeffrey Wecker leads a deep dive on data in financial services, with perspectives on the evolving landscape of data science, the advent of alternative data, the importance of data centricity, and the future for machine learning and AI. Read more.
11:20am–12:00pm Wednesday, 09/12/2018
Location: 1E 10/11 Level: Non-technical
JF Gagne (Element AI)
Average rating: ***..
(3.50, 4 ratings)
JF Gagne explains why 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.
11:20am–12:00pm Wednesday, 09/12/2018
Location: 1E 12/13 Level: Intermediate
Jennifer Prendki (Figure Eight)
Average rating: ****.
(4.38, 8 ratings)
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.
1:15pm–1:55pm Wednesday, 09/12/2018
Location: 1E 10/11 Level: Non-technical
Erin Coffman (Airbnb)
Average rating: *****
(5.00, 7 ratings)
Airbnb has open-sourced many high-leverage data tools, including Airflow, Superset, and the Knowledge Repo, but adoption of these tools across the company was relatively low. Erin Coffman offers an overview of Data University, launched to make data more accessible and utilized in decision making at Airbnb. Read more.
1:15pm–1:55pm Wednesday, 09/12/2018
Location: 1E 14 Level: Non-technical
Tony Baer (Ovum), Florian Douetteau (DATAIKU)
Average rating: ***..
(3.40, 5 ratings)
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.
2:05pm–2:45pm Wednesday, 09/12/2018
Location: 1E 14 Level: Intermediate
David Talby (Pacific AI)
Average rating: ****.
(4.40, 5 ratings)
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.
2:55pm–3:35pm Wednesday, 09/12/2018
Location: 1E 14 Level: Intermediate
Ted Malaska (Capital One), Jonathan Seidman (Cloudera)
Average rating: ****.
(4.00, 3 ratings)
Creating a successful big data practice in your organization presents new challenges in managing projects and teams. Ted Malaska and Jonathan Seidman share guidance and best practices to help technical leaders deliver successful projects from planning to implementation. Read more.
4:35pm–5:15pm Wednesday, 09/12/2018
Location: 1E 12/13 Level: Non-technical
Kimberly Nevala (SAS Institute)
Average rating: *****
(5.00, 1 rating)
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.
4:35pm–5:15pm Wednesday, 09/12/2018
Location: 1E 14 Level: Non-technical
Cassie Kozyrkov (Google)
Average rating: ****.
(4.30, 10 ratings)
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.
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 what's coming in the future. Read more.
11:20am–12:00pm Thursday, 09/13/2018
Location: 1E 10/11 Level: Non-technical
Average rating: ****.
(4.75, 4 ratings)
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, infrastructure, and more, Michelangelo D'Agostino shares concrete (and inexpensive) tips for keeping your data scientists engaged, productive, and adding business value. Read more.
11:20am–12:00pm Thursday, 09/13/2018
Location: 1E 14 Level: Intermediate
Mikio Braun (Zalando SE)
Average rating: **...
(2.75, 4 ratings)
In order to become "AI ready," an organization not only has to provide the right technical infrastructure for data collection and processing but also must learn new skills. Mikio Braun highlights three pieces companies often miss when trying to become AI ready: making the connection between business problems and AI technology, implementing AI-driven development, and running AI-based projects. Read more.
1:10pm–1:50pm Thursday, 09/13/2018
Location: 1E 14 Level: Non-technical
Brandy Freitas (Pitney Bowes)
Average rating: ****.
(4.50, 6 ratings)
Data science is an approachable field given the right framing. Often, though, practitioners and executives are describing opportunities using completely different languages. Join Brandy Freitas to develop context and vocabulary around data science topics to help build a culture of data within your organization. Read more.
4:20pm–5:00pm Thursday, 09/13/2018
Location: 1E 12/13 Level: Beginner
Francesca Lazzeri (Microsoft), Jaya Mathew (Microsoft)
Average rating: **...
(2.67, 6 ratings)
With the growing buzz around data science, many professionals want to learn how to become a data scientist—the role Harvard Business Review called the "sexiest job of the 21st century." Francesca Lazzeri and Jaya Mathew explain what it takes to become a data scientist and how artificial intelligence solutions have started to reinvent businesses. Read more.
4:20pm–5:00pm Thursday, 09/13/2018
Location: 1A 12/14 Level: Non-technical
Brian O'Neill (Designing for Analytics)
Average rating: *****
(5.00, 5 ratings)
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? Brian O'Neill explains why a "people first, technology second" mission—a design strategy, in other words—enables the best UX and business outcomes possible. Read more.
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.