Presented By
O’Reilly + Cloudera
O’Reilly + Cloudera
Make Data Work
29 April–2 May 2019
London, UK
London, UK
Schedule: AI and machine learning in the enterprise sessions
9:00 - 17:00 Monday, 29 April & Tuesday, 30 April
Average rating:
(1.86, 7 ratings)
Nijma Khan and Alberto Favaro offer a condensed introduction to key AI and machine learning concepts and techniques, showing you what is (and isn't) possible with these exciting new tools and how they can benefit your organization.
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9:00–17:00 Tuesday, 30 April 2019
Location: Capital Suite 12
Paco Nathan (derwen.ai),
Ganes Kesari (Gramener),
Alicia Williams (Google),
Semih Kumluk (Turkcell),
Simon Moritz (Ericsson),
Samuel Cristóbal (Innaxis),
Volker Schnecke (Novo Nordisk),
Julia Butter (Scout24),
Cecilia Marchi (Jakala),
Caroline Goulard (Dataveyes),
Marc Rind (ADP),
Juan Bengochea (Royal Caribbean Cruise Lines),
Aaronpal Dhanda (EasyJet )
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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13:30–17:00 Tuesday, 30 April 2019
Peter Aiken (Data BluePrint | DAMA International | Virginia Commonwealth University)
Average rating:
(3.43, 14 ratings)
Peter Aiken offers a more operational perspective on the use of data strategy, which is especially useful for organizations just getting started with data
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11:15–11:55 Wednesday, 1 May 2019
Moty Fania (Intel)
Average rating:
(3.83, 6 ratings)
Moty Fania shares his experience implementing a sales AI platform that handles processing of millions of website pages and sifts through millions of tweets per day. The platform is based on unique open source technologies and was designed for real-time data extraction and actuation.
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11:15–11:55 Wednesday, 1 May 2019
Mick Hollison (Cloudera)
Average rating:
(3.33, 3 ratings)
Managing your data securely is difficult, as is choosing the right machine learning tools and managing models and applications in compliance with regulation and law. Mick Hollison covers the risks and the issues that matter most and explains how to address them with an enterprise data cloud and by embracing your data center and the public cloud in combination.
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12:05–12:45 Wednesday, 1 May 2019
Laila Paszti (GTC Law Group PC & Affiliates)
Average rating:
(3.50, 2 ratings)
As companies commercialize novel applications of AI in areas such as finance, hiring, and public policy, there's concern that these automated decision-making systems may unconsciously duplicate social biases, with unintended societal consequences. Laila Paszti shares practical advice for companies to counteract such prejudices through a legal- and ethics-based approach to innovation.
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12:05–12:45 Wednesday, 1 May 2019
Pete Skomoroch (Workday)
Average rating:
(4.70, 10 ratings)
In the next decade, companies that understand how to apply machine intelligence will scale and win their markets. Others will fail to ship successful AI products that matter to customers. Pete Skomoroch details how to combine product design, machine learning, and executive strategy to create a business where every product interaction benefits from your investment in machine intelligence.
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12:05–12:45 Wednesday, 1 May 2019
Matthew Honnibal (Explosion AI)
Average rating:
(4.00, 4 ratings)
Matthew Honnibal shares "one weird trick" that can give your NLP project a better chance of success: avoid a waterfall methodology where data definition, corpus construction, modeling, and deployment are performed as separate phases of work.
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14:05–14:45 Wednesday, 1 May 2019
Average rating:
(4.83, 12 ratings)
Being good is hard. Being evil is fun and gets you paid more. Once more Duncan Ross and Francine Bennett explore how to do high-impact evil with data and analysis (and possibly AI). Make the maximum (negative) impact on your friends, your business, and the world—or use this talk to avoid ethical dilemmas, develop ways to deal responsibly with data, or even do good. But that would be perverse.
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14:55–15:35 Wednesday, 1 May 2019
Paco Nathan (derwen.ai)
Average rating:
(4.14, 7 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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17:25–18:05 Wednesday, 1 May 2019
Average rating:
(2.67, 3 ratings)
How do enterprises scale moving beyond one-off AI projects to making it reusable? Teresa Tung and Jean-Luc Chatelain explain how domain knowledge graphs—the technology behind today's internet search—can bring the same democratized experience to enterprise AI. They then explore other applications of knowledge graphs in oil and gas, financial services, and enterprise IT.
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11:15–11:55 Thursday, 2 May 2019
Brandy Freitas (Pitney Bowes)
Average rating:
(4.71, 7 ratings)
Data science is an approachable field given the right framing. Often, though, practitioners and executives are describing opportunities using completely different languages. Brandy Freitas walks you through developing context and vocabulary around data science topics to help build a culture of data within your organization.
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11:15–11:55 Thursday, 2 May 2019
David Dogon (Van Lanschot Kempen)
Average rating:
(4.75, 8 ratings)
David Dogon dives into a best practice use case for detecting fraud at a financial institution and details a dynamic and robust monitoring system that successfully detects unwanted client behavior. Join in to learn how machine learning models can provide a solution in cases where traditional systems fall short.
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12:05–12:45 Thursday, 2 May 2019
Average rating:
(3.00, 6 ratings)
Artificial intelligence and machine learning are now popularly used terms, but how do you make use of these techniques without throwing away the valuable knowledge of experienced employees? Rebecca Simmonds and Michael McCune delve into this idea with examples of how distributed machine learning frameworks fit together naturally with business rules management systems.
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12:05–12:45 Thursday, 2 May 2019
Vidya Raman (Cloudera)
Average rating:
(4.62, 8 ratings)
Not surprisingly, there's no single approach to embracing data-driven innovations within any industry vertical. However, some enterprises are doing a better job than others when it comes to establishing a culture, process, and infrastructure that lends itself to data-driven innovations. Vidya Raman explores some key foundational ingredients that span multiple industries.
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14:05–14:45 Thursday, 2 May 2019
Rosaria Silipo (KNIME)
Average rating:
(4.40, 10 ratings)
Rosaria Silipo shares a collection of past data science projects. While the structure is often similar—data collection, data transformation, model training, deployment—each required its own special trick, whether a change in perspective or a particular technique to deal with special case and special business questions.
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14:05–14:45 Thursday, 2 May 2019
Average rating:
(4.80, 5 ratings)
Features are key to driving impact with AI at all scales, allowing organizations to dramatically accelerate innovation and time to market. Willem Pienaar and Zhiling Chen explain how GOJEK, Indonesia's first billion-dollar startup, unlocked insights in AI by building a feature store called Feast, and the lessons they learned along the way.
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14:05–14:45 Thursday, 2 May 2019
Jack Norris (MapR Technologies)
Average rating:
(4.75, 4 ratings)
Many companies delay addressing core improvements in increasing revenues, reducing costs and risk exposure by tying changes to a to-be-hired data scientist. Drawing on three customer examples, Jack Norris explains how to achieve excellent results faster by starting with domain experience and helping developers and analysts better leverage data with available and understandable analytics.
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14:55–15:35 Thursday, 2 May 2019
Tal Doron (GigaSpaces)
Average rating:
(3.50, 2 ratings)
Technological advancements are transforming customer experience, and businesses are beginning to benefit from deep learning innovations to automate call center routing to the most proper agent. Tal Doron explains how to run deep learning models with Intel BigDL and Spark frameworks colocated on an in-memory computing platform to enhance the customer experience without the need for GPUs
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14:55–15:35 Thursday, 2 May 2019
Robert Cohen (Economic Strategy Institute)
Average rating:
(2.00, 2 ratings)
Robert Cohen discusses the skills that employers are seeking from employees in digital jobs, linked to the new software hierarchy driving digital transformation. Robert describes this software hierarchy as one that ranges from DevOps, CI/CD, and microservices to Kubernetes and Istio. This hierarchy is used to define the jobs that are central to data-driven digital transformation.
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16:35–17:15 Thursday, 2 May 2019
Average rating:
(3.00, 3 ratings)
Cybersecurity analysts are under siege to keep pace with the ever-changing threat landscape. The analysts are overworked as they are bombarded with and burned out by the sheer number of alerts that they must carefully investigate. Brennan Lodge and Jay Kesavan explain how to use a data science model for alert evaluations to empower your cybersecurity analysts.
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