26-28 November, 2019, Vilnius
Conference is over! See you next year.
Confirmed Talks
Jon Bratseth
Verizon Media, Norway
Talk
Big Data Serving: The Last Frontier. Processing and Inference at Scale in Real Time
The big data world has mature technologies for offline analysis and learning from data, but have lacked options for making data-driven decisions in real time. This talk introduces vespa.ai – a mature open source platform for storing, selecting processing and making model inferences over large data sets at end user request time.
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Kris van der Mast
VaHa, Belgium
Talk
Chat Bot Building Story - The Good, the Bad and the Unexpected
Since September 2016 I’ve been building bots and I want to share with you what we encountered during development, sometimes hilarious, sometimes not so much. Join me in this talk and gain insight on how to think about chat bots and how you can get started based on experience.
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Markus Winand
winand.at, Austria
Talk
More Than a Query Language: SQL in the 21st Century
“Great News–The Relational Model is Dead” was a prominent comment on the release of the new SQL standard in 1999. The message behind the provoking statement was that SQL has evolved beyond the relational model. As much as this move was discussed at that time, it took decades until database vendors caught up with this idiomatic change. Many developers haven’t heard of it until today.
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Dionizas Antipenkovas
Peekdata.io, Lithuania
Talk
Embedded Analytics through Data API or What Big Companies are Hiding from You
There are more than 20 years of experience of building amazing ETL and BI solutions and tools. These solutions proved being very useful and will serve us in the future.
However, such BI ecosystem was egoistically targeted for closed use cases with a focus on internal needs of companies using it, so only large companies allowed themselves offering great Analytical tools for their customers as self-service.
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Bradley Arsenault
Electric Brain Software Corporation, Canada
Best Practices for Building AI Datasets
Datasets are the most basic building blocks in AI systems, and the most innovative solutions often require manually collecting and labelling data. Yet most teams put their emphasis on magical models instead of solid, high quality datasets. In this talk, I will discuss many of the best practices for building AI datasets from scratch.
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The Joy of Designing Deep Neural Networks
Deep Learning is the most powerful class of learning algorithms that we have ever invented. In this talk, I will try to share the joy that I felt the first time I designed a deep neural network, and how simple intuitions on neural networks have lead me to great designs and amazing accuracy.
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Elena Lazovik
TNO, The Netherlands
Talk
Runtime Modifications of Distributed Big Data AI Models and Its Parameters
There has been phenomenal growth of interest in distributed data analysis with large software and other commercial entities. They are trying to construct the solutions that facilitate flexible, scalable and heterogeneous IT-infrastructures dealing with Big Data analysis. Distributed data processing platforms such as Hadoop or Apache Spark became a de-facto standard in the world of Big Data processing, and in cloud economy.
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Vygantas Butkus
Danske Bank, Lithuania
Talk
The Journey Through Real Estate Price Model Development
Together with his team, Vygantas has developed a model that predicts the selling price of homes in Denmark. The development was an exciting journey with many challenges and different attempts to overcome them. Sometimes the build process is more interesting than the final model itself. Thus, Vygantas is very happy to offer you a walkthrough of this experience with all the pains and joys encountered along the way.
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Emanuil Tolev
Elastic, UK
Talk
Better Data Exploration and Visualisation with Elasticsearch and Kibana
Data science has incredibly sophisticated analysis tools which are very effective, but rarely very convenient for their operators. In this live demo we will focus primarily on the prototyping and exploration phase of a data scientist’s workflow.
We’ll push a slice of a bigger dataset held in pandas into Elasticsearch. We’ll then use Kibana to build on the fly visualisations and dashboards, allowing us to explore and discover the data’s features in a more intuitive, faster way than writing commands in a jupyter notebook.
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Dr. Andreas Bühlmeier
Dr. Bühlmeier Consulting, Germany
Talk
Breakthroughs and the Future of (Deep) Reinforcement Learning
Due to recent successes in winning computer games against humans, Reinforcement Learning (RL)
received a lot of attention in the media.
This presentation will elaborate the foundation of RL and demonstrate how it is implemented.
Also, it will demonstrate how we can track and understand a system’s learning progress.
Most recent trends of RL research and applications in logistics and pharmacy will be discussed.
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