In this session we’ll briefly look at:
* The role of objective and subjective data, and how these influence data-driven insights;
* ‘Trans-contextual’ information and the implications for AI; 

In this talk we will discuss some of the latest advancements and real world applications of deep learning. At the end we will talk about the challenges and future opportunities in the field.

In the presentation Verdan will share our experience from designing and implementing a data discovery product powered by open-source technologies such as Apache Atlas and Amundsen (initially created by Lyft, and then moved to Linux Foundation).

Speaker will introduce a new machine learning based technique to detect exoplanets using the transit method. Machine learning and deep learning techniques have proven to be broadly applicable in various scientific research areas. He aims to exploit some of these methods to improve the conventional algorithm based approach used in astrophysics today to detect exoplanets.

Join the session to learn more and how to apply the successful analytics strategy to your own organization(s).

In this talk, Bas will present an architecture for streaming analytics solutions that covers many use cases that follow this pattern: actionable insights, fraud detection, log parsing, traffic analysis, factory data, the IoT, and others.

With the COVID-19 pandemic spreading across the globe, it’s becoming clear that few can avoid its reach, posing severe challenges to health services and having a range of related social and economic impacts.
This session will focus on the response to COVID-19 data analytics.

This talk presents the key insights that make AI/ML useful for marketing and demystifies the core technology and illustrates case studies where my team applied the technology.

 We discuss how Big Data, analytics and forecasting help to limit infections. We give insights of our journey from idea development to the actual situation and share our learnings.

Tomer Levi explains how the data engineering team at Fundbox uses AWS StepFunctions, Docker containers, and Spark to build a live serverless data orchestration platform, focusing on their decision to build a user-freindly yet powerful and scalable solution.

In this session Pasha would like to tell the story of creation of Kotlin for Apache Spark library. Root of idea, some details of Apache Spark implementation and so on.