26-28 November, 2019, Vilnius

Regular Prices End in:








Confirmed Talks

Ilia Kolochenko

ImmuniWeb, Switzerland


Practical Usage of AI in Cybersecurity

AI, Deep Learning and Intelligent Automation have become common words on cybersecurity vendors websites. Practical usage and necessity of AI/ML remains widely unsettled and is a subject of endless disputes among industry experts. Will robots replace humans or rather enable them to unleash the genius of their brain? Which technology is behind the AI acronym, what it can do and what it cannot do? To be explored and discussed during the talk.

Session Keywords

Artificial Intelligence
Application Security

Michael Shtelma

Databricks, Germany


Deep Learning at Scale: Distributed Training and Hyperparameter Search for Image Recognition Problems

Training complex image recognition model on a large dataset using one machine can be long and cumbersome. This talk focuses on methods and libraries, which allow us to train models on a dataset that does not fit into memory, or maybe even on the disk using multiple GPUs or even nodes. The ways of using multiple GPUs and nodes will be discussed and tradeoffs between different approaches will be compared. 

Session Keywords


Andy Bitterer

SAP, Gemany


Digital Business: Tomorrow is Already Here

Digital business is about intelligently connecting people, things, and businesses. It’s an infinite world of new possibilities for companies to reimagine their business models, the way they work, and how they compete. New technologies like machine learning, the Internet of everything, blockchain, cloud, and the big data platform will transform value chains to enable completely new ways of doing business and our way of life. Hear how you can deliver a innovative customer experience at scale, with a fully-integrated front- and back-end operations based a solid digital core.

Session Keywords

Data Analytics
Use Cases

Stefan Reiser

LINK Institute, Switzerland


Turning a Wasting into a Learning Culture - Combining NLP and Neural Networks to truly Understand and Predict Customers' Behaviour

Most of the customer feedback of companies around the globe is being wasted, as it is not used to learn, derive insights or to optimize products and processes. At the same time, the amount of customer survey and observation data within companies is growing at heavy speed. The presentation will introduce levers on how to cope with this phenomenon and illustrate, which role Data Science and Machine Learning should Play from an analytical and business perspective.

Session Keywords

Customer Analysis
Data Science
Predictive Analytics

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.

Session Keywords

Data Collection
Machine Learning

Michał Dyrda

Philip Morris International, Poland


Data Science at PMI - The Tools of The Trade

Data Science is not a one man show. It is a team effort that requires every team member to master the tools of the trade. This is extremely important for effectively putting data science to work in a global organization. In this talk Michal would like to share with you the best practices to start, develop and ship data science products developed inside PMI – the best practices and tools, currently in use by 40+ data scientists across four different location, where data science labs of PMI were established in 2017.

Session Keywords

Best Practices for Data Science

Magnus Runesson

Tink, Sweden


Optimize your Data Pipeline without Rewriting it

It is not fast enough! That is one of the more common responses to a data engineer when putting a data pipeline in production. It is easy to dig down into the code and try to optimize it. My experience as a data engineer shows me that it is often easier and more efficient, both in time spent and outcome, to focus on a more holistic view of the pipeline.

Session Keywords

Data Pipeline

Valdas Maksimavičius

Cognizant, Lithuania


Making Data Scientists Productive in Azure

Doing data science today is far more difficult that it will be in the next 5-10 years. Sharing, collaborating on data science workflows in painful, pushing models into production is challenging.
Let’s explore what Azure provides to ease Data Scientists’ pains. What tools and services can we choose based on a problem definition, skillset or infrastructure requirements?

Session Keywords

Azure ML

Alexander Slotte

Excella, USA


Real-Time Data Streaming with Azure Stream Analytics

It’s imperative in today’s world to be able to make split second decisions based on real-time data. Reports based on batch data are great for looking back at trends and potentially making long-term decision, but old data is in many cases already obsolete, and the opportunity to have an actionable impact on the success of a specific process may have been lost.

Session Keywords

Real-time Analytics