Barr will introduce the concept of “data downtime” — periods of time when data is partial, erroneous, missing or otherwise inaccurate. Data downtime is highly costly for organizations, yet is often addressed ad hoc. She’ll discuss why data downtime matters to the data industry and tactics best-in-class organizations use to address it — including org structure, culture, and technology.

In his talk Yiannis Kanellopoulos will present an approach on how an ML model can be evaluated in terms of its Fairness, Accountability and Transparency. Using examples of case studies (from industrial and publicly available datasets) Yiannis will share insights and the benefits one can get by making a ML model accountable, transparent and trying to mitigate its biases.

From discussing what is AI to practical case studies of AI, Kane will discuss how companies in Hong Kong and world wide uses AI to create business values.

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.

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.

The possibility of using neuro-fuzzy networks also allows the features of neural networks to be combined with fuzzy logic, thus providing automatic extraction of rule bases in the usual ‘if…then…’ form.

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.