Presented By O'Reilly and Cloudera
Make Data Work
22–23 May 2017: Training
23–25 May 2017: Tutorials & Conference
London, UK

Schedule: AI sessions

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9:0012:30 Tuesday, 23 May 2017
SOLD OUT
Data science and advanced analytics
Location: Capital Suite 13
Level: Intermediate
Alison Lowndes (NVIDIA)
Average rating: **...
(2.50, 4 ratings)
Alison Lowndes leads a hands-on exploration of approaches to the challenging problem of detecting if an object of interest is present within an image and, if so, recognizing its precise location within the image. Along the way, Alison walks you through testing three different approaches to deploying a trained DNN for inference. Read more.
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9:0012:30 Tuesday, 23 May 2017
Location: Capital Suite 15
Doron Reuter (ING), Aida Mehonic (ASI Data Science), Colin White (Goldman Sachs), Simon Wardley (Leading Edge Forum), Tanvi Singh (Credit Suisse), Olivier de Garrigues (Trifacta)
Finance is information. From analyzing risk and detecting fraud to predicting payments and improving customer experience, data technologies are transforming the financial industry. And we're diving deep into this change with a new day of data-meets-finance talks, tailored for Strata Data Conference events in the world's financial hubs. Read more.
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9:0017:00 Tuesday, 23 May 2017
Location: London Suite 2/3
Angie Ma (ASI), Ben Lorica (O'Reilly Media), Ira Cohen (Anodot), Yingsong Zhang (ASI Data Science), Ali Hürriyetoglu (Statistics Netherlands), Nelleke Oostdijk (Radboud University), Robin Senge (inovex GmbH), Mathew Salvaris (Microsoft), Miguel Gonzalez-Fierro (Microsoft), Amitai Armon (Intel), Yahav Shadmi (Intel), Kay Brodersen (Google), Ding Ding (Intel), Alan Mosca (Sendence | Birkbeck, University of London), Eduard Vazquez (Cortexica Vision Systems), Aida Mehonic (ASI Data Science), David Barber (Department of Computer Science, UCL)
A full day of hardcore data science, exploring emerging topics and new areas of study made possible by vast troves of raw data and cutting-edge architectures for analyzing and exploring information. Along the way, leading data science practitioners teach new techniques and technologies to add to your data science toolbox. Read more.
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10:2510:40 Wednesday, 24 May 2017
M. C. Srivas (Uber)
Average rating: ***..
(3.95, 19 ratings)
M. C. Srivas covers the technologies underpinning the big data architecture at Uber and explores some of the real-time problems Uber needs to solve to make ride sharing as smooth and ubiquitous as running water, explaining how they are related to real-time big data analytics. Read more.
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11:1511:55 Wednesday, 24 May 2017
Data science and advanced analytics
Location: Capital Suite 8/9
Level: Beginner
Yishay Carmiel (IntelligentWire)
Average rating: ***..
(3.00, 1 rating)
For years, people have been talking about the great promise of conversation AI. Recently, deep learning has taken us a few steps further toward achieving tangible goals, making a big impact on technologies like speech recognition and natural language processing. Yishay Carmiel offers an overview of the impact of deep learning, recent breakthroughs, and challenges for the future. Read more.
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14:0514:45 Wednesday, 24 May 2017
Business case studies
Location: Hall S21/23 (A)
Level: Beginner
Kazunori Sato (Google)
Average rating: ****.
(4.20, 5 ratings)
TensorFlow is democratizing the world of machine intelligence. With TensorFlow (and Google's Cloud Machine Learning platform), anyone can leverage deep learning technology cheaply and without much expertise. Kazunori Sato explores how a cucumber farmer, a car auction service, and a global insurance company adopted TensorFlow and Cloud ML to solve their real-world problems. Read more.
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14:5515:35 Wednesday, 24 May 2017
Business case studies
Location: Capital Suite 7
Level: Intermediate
Josef Viehhauser (BMW Group), Dominik Schniertshauer (BMW Group)
Average rating: ****.
(4.60, 5 ratings)
Data-driven solutions based on machine and deep learning are gaining momentum in the automotive industry beyond autonomous driving. Josef Viehhauser and Dominik Schniertshauer explore use cases from the BMW Group where novel machine-learning pipelines (such as those based on XGBoost and convolutional neural nets, for example) support a broad variety of business stakeholders. Read more.
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16:3517:15 Wednesday, 24 May 2017
Data science and advanced analytics
Location: Capital Suite 7
Level: Beginner
Laura Froelich (Think Big Analytics, a Teradata Company)
Average rating: ***..
(3.00, 3 ratings)
Laura Frolich explores applications of deep learning in companies—looking at practical examples of assessing the opportunity for AI, phased adoption, and lessons going from research to prototype to scaled production deployment—and discusses the future of enterprise AI. Read more.
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17:2518:05 Wednesday, 24 May 2017
Emerging Technologies, Strata Business Summit
Location: Capital Suite 15/16
Level: Non-technical
Adam Smith (Automated Insights)
Average rating: ***..
(3.75, 4 ratings)
Natural language generation, the branch of AI that turns raw data into human-sounding narratives, is coming into its own in 2016. Adam Smith explores the real-world advances in NLG over the past decade and then looks ahead to the next. Computers are already writing finance, sports, ecommerce, and business intelligence stories. Find out what—and how—they’ll be writing by 2026. Read more.
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17:2518:05 Wednesday, 24 May 2017
Level: Beginner
Dr.-Ing. Michael Nolting (Volkswagen Commercial Vehicles)
Average rating: *....
(1.67, 6 ratings)
It is nearly impossible to sample enough training data initially to prevent autonomous driving accidents on the road, as has been sadly proven by Tesla’s autopilot. Michael Nolting explains that to overcome this problem, a real-time system has to be created to detect dangerous runtime situations in real time, a process much like website monitoring. Read more.
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11:1511:55 Thursday, 25 May 2017
Level: Non-technical
Martin Goodson (Evolution AI), Andrew Crisp (Dun & Bradstreet)
Average rating: ****.
(4.00, 8 ratings)
Martin Goodson gives a tell-all account of an ultimately successful installation of a deep learning system in an enterprise environment. Andy Crisp then shares insights into the challenges of integrating artificial intelligence systems into real-world business processes. Read more.
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12:0512:45 Thursday, 25 May 2017
Data science and advanced analytics
Location: Hall S21/23 (A)
Level: Intermediate
Barbara Fusinska (Katacoda)
Average rating: ***..
(3.00, 5 ratings)
The popularity of deep learning is due in part to its capabilities in recognizing patterns from inputs such as images or sounds. Barbara Fusinska offers an overview of Microsoft Cognitive Toolbox, an open source framework offering various modules and algorithms enabling machines to learn like a human brain. Read more.