26-28 November, 2019, Vilnius

Early Bird Ends In:

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26-28 November, 2019, Vilnius

Early Bird Ends In:

Day(s)

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Hour(s)

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Minute(s)

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Second(s)

Confirmed Talks

Ilia Kolochenko

ImmuniWeb, Switzerland

Talk

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

Application Security
Cybersecurity
AI
ML

Michael Shtelma

Databricks, Germany

Talk

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

Deep Learning
Petastorm
Keras
Horovod
Databricks
Spark
ML

Andy Bitterer

SAP, Gemany

Talk

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

Analytics
Strategy
Use Case
Futurist

Stefan Reiser

LINK Institute, Switzerland

Talk

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

Artificial Intelligence
Big Data
Data Science
Predictive Analytics

Bradley Arsenault

Electric Brain Software Corporation, Canada

Talk

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
Annotation
Machine Learning

Mark West

Bouvet Norge AS, Norway

Talk

A Practical-ish Introduction to Data Science

Data Science has been described as the sexiest job of the 21st Century. But what is Data Science? And what has Machine Learning got to do with all of this?
In this talk, Mark will share insights and knowledge that he has gained from building up a Data Science department from scratch.

Session Keywords

Data Science
Machine Learning
Talk

Deconstructing Deep Learning

In this session attendees can expect a mathematics and jargon free introduction to Deep Learning!

1. Mark will start by defining the general principles and theory behind Deep Learning and Artificial Neural Networks.

2. Next Mark will demonstrate how Deep Learning can be utilised for Image Processing, by taking a look at an implementation of a Convolutional Neural Network (CNN).

Session Keywords

DeepLearning
TensorFlow
Keras

Magnus Runesson

Tink, Sweden

Talk

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

Pipeline
Operation
Data Driven
Optimize

Valdas Maksimavičius

Cognizant, Lithuania

Talk

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
Databricks
MLflow
Azure ML

Alexander Slotte

Excella, USA

Talk

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

Stream-processing
Real-time Analytics
Data-pipelines