Presented By O'Reilly and Cloudera
Make Data Work
31 May–1 June 2016: Training
1 June–3 June 2016: Conference
London, UK

Media conference sessions

14:05–14:45 Friday, 3/06/2016
Marton Trencseni (Facebook)
At first glance A/B testing is a simple matter: take a few numbers, put them into an online calculator, and read off the statistical significance. But in fact it's a complex topic with amazing opportunities (and pitfalls) for organizations. Marton Trencseni offers a deep dive into A/B testing to provide attendees the information needed to improve their organizations' experimentation cultures.
9:50–10:10 Wednesday, 1/06/2016
carme artigas (Synergic Partners)
The most important challenge companies face in realizing the value of big data is implementing a cultural change to become a data-driven organization. Carme Artigas shares real-world examples focusing on the business side of this technology disruption to show how big data is transforming different industries including retail, insurance, telco, and digital businesses.
12:00–12:30 Wednesday, 1/06/2016
Piotr Mirowski (Google DeepMind)
Piotr Mirowski looks under the hood of recurrent neural networks and explains how they can be applied to speech recognition, machine translation, sentence completion, and image captioning.
9:05–9:30 Wednesday, 1/06/2016
Mounia Lalmas (Spotify)
Mounia Lalmas offers an overview of work aimed at understanding the user preclick experience of ads and building a learning framework to identify ads with low preclick quality.
14:05–14:45 Friday, 3/06/2016
Kate O'Neill (KO Insights)
The metaphors used online have always borrowed heavily from the offline world, but as our online and offline worlds converge, the biggest opportunities for innovative experiences will come from blending them intentionally. Kate O’Neill examines how the meaning and understanding of place relates to identity, culture, and intent and how we can shape our audiences' experiences more meaningfully.
10:00–10:30 Wednesday, 1/06/2016
Danny Bickson (1972)
A Netflix competition triggered a major academic research effort in recommender systems. However, there is still a big gap between academic research and industry. Danny Bickson covers the current state of recommender systems in industry and explains why, while user historical purchase data is understood very well, recommenders based on images and text are just starting to pick up.
14:55–15:35 Thursday, 2/06/2016
Carl Steinbach (LinkedIn)
Carl Steinbach offers an overview of Dali, LinkedIn's collection of libraries, services, and development tools that are united by the common goal of providing a dataset API for Hadoop.
12:05–12:45 Thursday, 2/06/2016
Sherry Moore (Google)
TensorFlow is an open source software library for numerical computation with a focus on machine learning. Its flexible architecture makes it great for research and production deployment. Sherry Moore offers a high-level introduction to TensorFlow and explains how to use it to train machine-learning models to make your next application smarter.
14:05–14:45 Thursday, 2/06/2016
Paul Shannon (7digital Group Plc), Alan Hannaway (7digital)
Can our real-time distributed data systems help predict whether high-resolution audio is the future of digital music? What about content curation? Paul Shannon and Alan Hannaway explore the future of music services through data and explain why 7digital believes well-curated, high-resolution listening experiences are the future of digital music services.