Presented By
O’Reilly + Cloudera
O’Reilly + Cloudera
Make Data Work
March 25-28, 2019
San Francisco, CA
San Francisco, CA
Schedule: AI and machine learning in the enterprise sessions
9:00am - 5:00pm Monday, March 25 & Tuesday, March 26
Average rating:
(4.50, 4 ratings)
Michael Li and Rich Ott offer a nontechnical overview of AI and data science. Learn common techniques, how to apply them in your organization, and common pitfalls to avoid. You’ll pick up the language and develop a framework to be able to effectively engage with technical experts and utilize their input and analysis for your business’s strategic priorities and decision making.
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9:00am–12:30pm Tuesday, March 26, 2019
Average rating:
(4.00, 6 ratings)
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. Jonathan Seidman and Ted Malaska share guidance and best practices from planning to implementation based on years of experience working with companies to deliver successful data projects.
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9:00am–12:30pm Tuesday, March 26, 2019
Average rating:
(4.60, 15 ratings)
The honeymoon era of data science is ending, and accountability is coming. Successful data science leaders must deliver measurable impact on an increasing share of an enterprise's KPIs. Joshua Poduska, Kimberly Shenk, and Mac Steele explain how leading organizations take a holistic approach to people, process, and technology to build a sustainable competitive advantage.
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9:00am–5:00pm Tuesday, March 26, 2019
Location: 2022
Alex Kudriashova (Astro Digital),
Jonathan Francis (Starbucks),
JoLynn Lavin (General Mills),
Robin Way (Corios),
June Andrews (GE),
Kyungtaak Noh (SK Telecom),
Taposh DuttaRoy (Kaiser Permanente),
Sabrina Dahlgren (Kaiser Permanente),
Craig Rowley (Columbia Sportswear),
Ambal Balakrishnan (IBM),
Benjamin Glicksberg (UCSF),
Patrick Lucey (Stats Perform),
Rhonda Textor (True Fit)
Hear practical insights from household brands and global companies: the challenges they tackled, approaches they took, and the benefits—and drawbacks—of their solutions.
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1:30pm–5:00pm Tuesday, March 26, 2019
Andrew Burt (bnh.ai),
Steven Touw (Immuta),
richard geering (Immuta),
Joseph Regensburger (Immuta),
Alfred Rossi (Immuta)
Average rating:
(5.00, 2 ratings)
As ML becomes increasingly important for businesses and data science teams alike, managing its risks is quickly becoming one of the biggest challenges to the technology’s widespread adoption. Join Andrew Bur, Steven Touw, Richard Geering, Joseph Regensburger, and Alfred Rossi for a hands-on overview of how to train, validate, and audit machine learning models (ML) in practice.
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1:30pm–5:00pm Tuesday, March 26, 2019
Average rating:
(4.25, 4 ratings)
Many teams are still run as if data science is mainly about experimentation, but those days are over. Now it must offer turnkey solutions to take models into production. Sourav Day and Alex Ng explain how to streamline an ML project and help your engineers work as an integrated part of your production teams, using a Lean AI process and the Orbyter package for Docker-first data science.
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1:30pm–5:00pm Tuesday, March 26, 2019
Chi-Yi Kuan (LinkedIn),
Tiger Zhang (LinkedIn),
Xiaojing Dong (LinkedIn),
Burcu Baran (LinkedIn),
Emily Huang (LinkedIn)
Average rating:
(4.43, 14 ratings)
Thanks to the rapid growth in data resources, business leaders now appreciate the importance (and the challenge) of mining information from data. Join in as a group of LinkedIn's data scientists share their experiences successfully leveraging emerging techniques to assist in intelligent decision making.
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9:30am–9:40am Wednesday, March 27, 2019
Location: Ballroom
Ben Lorica (O'Reilly)
Average rating:
(4.21, 29 ratings)
Keynote with Ben Lorica
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11:00am–11:40am Wednesday, March 27, 2019
Average rating:
(4.50, 4 ratings)
Jian Chang and Sanjian Chen outline the design of the AI engine on Alibaba's TSDB service, which enables fast and complex analytics of large-scale retail data. They then share a successful case study of the Fresh Hema Supermarket, a major “new retail” platform operated by Alibaba Group, highlighting solutions to the major technical challenges in data cleaning, storage, and processing.
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11:00am–11:40am Wednesday, March 27, 2019
Mike Olson (Cloudera)
Average rating:
(3.80, 5 ratings)
It's easier than ever to collect data, but managing it securely in compliance with regulations and legal constraints is harder. Mike Olson discusses the risks and the issues that matter most and explains how an enterprise data cloud that embraces your data center and the public cloud in combination can address them, delivering real business results for your organization.
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11:50am–12:30pm Wednesday, March 27, 2019
Sarah Aerni (Salesforce)
Average rating:
(4.25, 4 ratings)
How does Salesforce make data science an Agile partner to over 100,000 customers? Sarah Aerni shares the nuts and bolts of the platform and details the Agile process behind it. From open source autoML library TransmogrifAI and experimentation to deployment and monitoring, Sarah covers the tools that make it possible for data scientists to rapidly iterate and adopt a truly Agile methodology.
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11:50am–12:30pm Wednesday, March 27, 2019
Maryam Jahanshahi (TapRecruit)
Average rating:
(4.80, 5 ratings)
Hiring teams largely rely on both intuition and experience to scout talent for data science and data engineering roles. Drawing on results from analyzing over 15 million jobs and their outcomes, Maryam Jahanshahi interrogates these “common sense” judgments to determine whether they help or hurt hiring of data scientists and engineers.
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11:50am–12:30pm Wednesday, March 27, 2019
Melinda Han Williams (Dstillery)
Average rating:
(4.86, 14 ratings)
Customer segmentation based on coarse survey data is a staple of traditional market research. Melinda Han Williams explains how Dstillery uses neural networks to model the digital pathways of 100M consumers and uses the resulting embedding space to cluster customer populations into fine-grained behavioral segments and inform smarter consumer insights—in the process, creating a map of the internet.
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2:40pm–3:20pm Wednesday, March 27, 2019
Average rating:
(4.79, 14 ratings)
A/B testing has revealed the fallibility in human intuition that typically drives business decisions. Eric Colson and Daragh Sibley describe some types of systematic errors domain experts commit, explain how cognitive biases arise from heuristic reasoning processes, and share several mechanisms to mitigate these human limitations and improve decision making.
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4:20pm–5:00pm Wednesday, March 27, 2019
Paco Nathan (derwen.ai)
Average rating:
(3.67, 6 ratings)
Effective data governance is foundational for AI adoption in enterprise, but it's an almost overwhelming topic. Paco Nathan offers an overview of its history, themes, tools, process, standards, and more. Join in to learn what impact machine learning has on data governance and vice versa.
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5:10pm–5:50pm Wednesday, March 27, 2019
Dave Stuart (Department of Defense )
Average rating:
(4.38, 8 ratings)
Many organizations look to add data science to their skill portfolios through the hiring of data science experts. Dave Stuart shares a complementary way to build a data science-savvy workforce that nets tremendous value by using Jupyter to add introductory data science practices to domain experts and business analysts.
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5:10pm–5:50pm Wednesday, March 27, 2019
BARKHA GVALANI (GV)
Average rating:
(2.50, 4 ratings)
How do you decide if you should invest in upskilling business teams? The question is no longer "if" but "when" and "how." Barkha Gvalani shares a framework for developing and delivering analytics training to nontechnical users.
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5:10pm–5:50pm Wednesday, March 27, 2019
Kevin Moore (Salesforce)
Average rating:
(4.50, 2 ratings)
Kevin Moore walks you through how TransmogrifAI—Salesforce's open source AutoML library built on Spark—automatically generates models that are automatically customized to a company's dataset and use case and provides insights into why the model is making the predictions it does.
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11:00am–11:40am Thursday, March 28, 2019
Ram Shankar Siva Kumar (Microsoft (Azure Security))
Average rating:
(4.33, 3 ratings)
How can we guarantee that the ML system we develop is adequately protected from adversarial manipulation? Ram Shankar Kumar shares a framework and corresponding best practices to quantitatively assess the safety of your ML systems.
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11:00am–11:40am Thursday, March 28, 2019
Marc Paradis (UnitedHealth Group)
Average rating:
(4.75, 4 ratings)
Data Science University (DSU) was established to bring analytics education to UnitedHealth Group, the world’s largest healthcare company, with over 270,000 employees. Marc Paradis explains how DSU was built out over time in an era of rapidly changing analytics technology and capabilities in an industry ripe for disruption, covering the challenges faced and lessons learned.
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11:50am–12:30pm Thursday, March 28, 2019
Average rating:
(4.50, 2 ratings)
Today, normal growth isn't enough—you need hockey-stick levels of growth. Sales and marketing orgs are looking to AI to "growth hack" their way to new markets and segments. Ken Johnston and Ankit Srivastava explain how to use mutual information at scale across massive data sources to help filter out noise and share critical insights with new cohort of users, businesses, and networks.
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1:50pm–2:30pm Thursday, March 28, 2019
Stuart Buck (Arnold Ventures)
Average rating:
(4.50, 4 ratings)
Academic research has been plagued by a reproducibility crisis in fields ranging from medicine to psychology. Stuart Buck explains how to take precautions in your data analysis and experiments so as to avoid those reproducibility problems.
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2:40pm–3:20pm Thursday, March 28, 2019
Average rating:
(3.67, 6 ratings)
What happens when you have a data science organization but no data engineering organization? Jesse Anderson and Thomas Goolsby explain what happened at USAA without data engineering, how they fixed it, and the results since.
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2:40pm–3:20pm Thursday, March 28, 2019
Till Bergmann (Salesforce)
Average rating:
(3.67, 6 ratings)
A problem in predictive modeling data is label leakage. At enterprise companies such as Salesforce, this problem takes on monstrous proportions as the data is populated by diverse business processes, making it hard to distinguish cause from effect. Till Bergmann explains how Salesforce—which needs to churn out thousands of customer-specific models for any given use case—tackled this problem.
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3:50pm–4:30pm Thursday, March 28, 2019
Patrick Miller (Civis Analytics)
Average rating:
(3.40, 5 ratings)
Brands that test the content of ads before they are shown to an audience can avoid spending resources on the 11% of ads that cause backlash. Using a survey experiment to choose the best ad typically improves effectiveness of marketing campaigns by 13% on average, and up to 37% for particular demographics. Patrick Miller explores data collection and statistical methods for analysis and reporting.
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3:50pm–4:30pm Thursday, March 28, 2019
Average rating:
(4.00, 1 rating)
Knowledge of customers' location and travel patterns is important for many companies, including German telco service operator Deutsche Telekom. Václav Surovec and Gabor Kotalik explain how a commercial roaming project using Cloudera Hadoop helped the company better analyze the behavior of its customers from 10 countries and provide better predictions and visualizations for management.
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4:40pm–5:20pm Thursday, March 28, 2019
Michael Li (The Data Incubator)
Average rating:
(3.75, 4 ratings)
As their data and AI teams scale from one to thousands of employees and the maturity of their analytics capabilities evolve, companies find that the analytics journey is not always smooth. Drawing on experiences gleaned from dozens of clients, Michael Li discusses organizational growing pains and the best practices that successful executives have adopted to scale and grow their team.
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