Presented By
O’Reilly + Cloudera
Make Data Work
29 April–2 May 2019
London, UK

Schedule: AI and machine learning in the enterprise sessions

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9:00 - 17:00 Monday, 29 April & Tuesday, 30 April
Strata Business Summit
Location: Capital Suite 17
Angie Ma (ASI Data Science)
Angie Ma and Jonny Howell 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. Read more.
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9:0017:00 Tuesday, 30 April 2019
Location: Capital Suite 12
Alistair Croll (Solve For Interesting), Ganes Kesari (Gramener Inc), Alicia Williams (Google), Simon Moritz (Ericsson AB), Samuel Cristóbal (Innaxis), Volker Schnecke (Novo Nordisk), Julia Butter (Scout24 AG), Cecilia Marchi (Jakala), Caroline GOULARD (Dataveyes)
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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13:3017:00 Tuesday, 30 April 2019
Strata Business Summit
Location: Capital Suite 8
Peter Aiken (Data BluePrint, DAMA International, Virginia Commonwealth University)
The presents a more operational perspective on the use of data strategy that is especially useful for organizations just getting started with data Read more.
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11:1511:55 Wednesday, 1 May 2019
Data Engineering and Architecture
Location: Capital Suite 8/9
Moty Fania (Intel)
In this session, Moty Fania will share his experience of implementing a Sales AI platform. It handles processing of millions of website pages and sifting thru millions of tweets per day. The platform is based on unique open source technologies and was designed for real-time, data extraction and actuation. This session highlights the key learnings with a thorough review of the architecture. Read more.
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11:1511:55 Wednesday, 1 May 2019
Mike Olson (Cloudera)
It's easier than ever to collect data -- but managing it securely, in compliance with regulations and legal constraints is harder. There are plenty of tools that promise to bring machine learning techniques to your data -- but choosing the right tools, and managing models and applications in compliance with regulation and law is quite difficult. Read more.
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12:0512:45 Wednesday, 1 May 2019
Law and Ethics, Strata Business Summit
Location: Capital Suite 10/11
Laila Paszti (GTC Law Group PC & Affiliates)
As companies commercialize novel applications of AI in areas such as finance, hiring, and public policy, there is concern that these automated decision-making systems may unconsciously duplicate social biases, with unintended societal consequences. This talk will provide practical advice for companies to counteract such prejudices through a legal and ethics based approach to innovation. Read more.
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12:0512:45 Wednesday, 1 May 2019
Pete Skomoroch (Workday)
Companies that understand how to apply machine intelligence will scale and win their respective markets over the next decade. Others will fail to ship successful AI products that matter to customers. This talk describes 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. Read more.
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12:0512:45 Wednesday, 1 May 2019
Data Science, Machine Learning & AI
Location: Expo Hall (Capital Hall N24)
Matthew Honnibal (Explosion AI)
In this talk, I'll discuss "one weird trick" that can give your NLP project a better chance of success. The advice is this: avoid a "waterfall" methodology where data definition, corpus construction, modelling and deployment are performed as separate phases of work. Read more.
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14:0514:45 Wednesday, 1 May 2019
Law and Ethics, Strata Business Summit
Location: Capital Suite 10/11
Duncan Ross (TES Global), Francine Bennett (Mastodon C)
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. Read more.
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14:5515:35 Wednesday, 1 May 2019
Paco Nathan (derwen.ai)
Data governance is an almost overwhelming topic. This talk surveys history, themes, plus a survey of tools, process, standards, etc. Mistakes imply data quality issues, lack of availability, and other risks that prevent leveraging data. OTOH, compliance issues aim to preventing risks of leveraging data inappropriately. Ultimately, risk management plays the "thin edge of the wedge" in enterprise. Read more.
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16:3517:15 Wednesday, 1 May 2019
Data Science, Machine Learning & AI
Location: Capital Suite 14
Divya Choudhary (GOJEK)
Data scientists around the globe would agree that addresses are the most unorganised textual data. Structuring addresses has almost led to a new stream of NLP itself. Who would've imagined that address text data can be used to develop one of the coolest product feature of finding the most precise pick up/drop-off locations for e-commerce, logistics, food delivery or ride/car services companies! Read more.
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17:2518:05 Wednesday, 1 May 2019
Teresa Tung (Accenture Labs), Jean-Luc Chatelain (Accenture)
How do enterprises scale moving beyond one-off AI projects to making it re-usable? Teresa Tung and Jean-Luc Chatelain explain how domain knowledge graphs—the same technology behind today's Internet search—can bring the same democratized experience to enterprise AI. Beyond search applications, we show other applications of knowledge graphs in oil & gas, financial services, and enterprise IT. Read more.
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11:1511:55 Thursday, 2 May 2019
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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11:1511:55 Thursday, 2 May 2019
Data Science, Machine Learning & AI
Location: Capital Suite 14
Charlotte Werger (Van Lanschot Kempen)
This talk discusses a best practice use case for detecting fraud at a financial institution. Where traditional systems fall short, machine learning models can provide a solution. Sifting through large amounts of transaction data, external hit lists, and unstructured text data we managed to build a dynamic and robust monitoring system that successfully detects unwanted client behavior. Read more.
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12:0512:45 Thursday, 2 May 2019
Data Engineering and Architecture
Location: Capital Suite 10/11
Rebecca Simmonds (Red Hat), Michael McCune (Red Hat)
Artificial intelligence and machine learning are now popularly used terms but how do we make use of these techniques, without throwing away the valuable knowledge of experienced employees. This session will delve into this idea with examples of how distributed machine learning frameworks fit together naturally with business rules management systems. Read more.
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12:0512:45 Thursday, 2 May 2019
Vidya Raman (Cloudera)
Not surprisingly, there is no single approach to embracing data-driven innovations within any industry vertical. However, there are some enterprises that are doing a better job than others when it comes to establishing a culture, process and infrastructure that lends itself to data-driven innovations. In this talk, we will share some key foundational ingredients that span multiple industries. Read more.
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14:0514:45 Thursday, 2 May 2019
Case studies, Strata Business Summit
Location: Capital Suite 12
Rosaria Silipo (KNIME)
This is a collection of past data science projects. While the structure is often similar - data collection, data transformation, model training, deployment - each one of them has needed some special trick. It was either the change in perspective or a particular techniques to deal with special case and special business questions the turning point in implementing the data science solution. Read more.
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14:0514:45 Thursday, 2 May 2019
Data Engineering and Architecture
Location: Capital Suite 8/9
Willem Pienaar (GO-JEK), Zhi Ling Chen (GO-JEK)
Features are key to driving impact with AI at all scales. By democratizing the creation, discovery, and access of features through a unified platform, organizations are able to dramatically accelerate innovation and time to market. Find out how GO-JEK, Indonesia's first billion-dollar startup, built a feature platform to unlock insights in AI, and the lessons they learned along the way. Read more.
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14:5515:35 Thursday, 2 May 2019
Data Science, Machine Learning & AI
Location: Capital Suite 17
Yoav Einav (GigaSpaces)
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. This session will discuss how Deep Learning models can be run with Intel BigDL and Spark frameworks co-located on an in-memory computing platform to enhance the customer experience without the need for GPUs Read more.
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14:5515:35 Thursday, 2 May 2019
Culture and organization, Strata Business Summit
Location: Capital Suite 12
Robert Cohen (Economic Strategy Institute)
This talk describes the skills that employers are seeking from employees in digital jobs – linked to the new software hierarchy driving digital transformation. We describe 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. Read more.
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16:3517:15 Thursday, 2 May 2019
Data Science, Machine Learning & AI
Location: Capital Suite 14
Brennan Lodge (Goldman Sachs), Jay Kesavan (Bowery Analytics LLC)
Cyber security analysts are under siege to keep pace with the ever-changing threat landscape. The analysts are overworked, burnout and bombarded with the sheer number of alerts that they must carefully investigate. To empower our cyber security analysts we can use a data science model for alert evaluations. Read more.