BIG DATA CONFERENCE
CEO & Co-Founder of Data/Analytics startup backed by top Silicon Valley investors
USA, Monte Carlo
Making Data Downtime a Pillar of Your Data Strategy
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.
Trust and Quality in the Era of Software 2.0
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.
Data Science Case Studies and Formulation of AI Roadmap
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.
Towards Enterprise-Grade Data Discovery at ING with Apache Atlas and Amundsen
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).
Exoplanet Detection Using Machine Learning
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.
Streaming Processing – an Overview of the Concepts, Architecture and Technology of Doing Data Science on Real-Time Data
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.
Covid-19: Big Data Analytics and Artificial Intelligence
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 application of Machine Learning to the Modelling of Time-Series of Atmospheric Pollution Data
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.
The Intuition Behind the Use of M.L. in Marketing 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.
Stopping Public Transport Coronavirus Infections with Big Data
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.
Orchestrating Data Workflows Using a Fully Serverless Architecture
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.
Kotlin for Apache Spark: Love to Frankenstein’s Monster
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.