Big Data Days 2019

 October 8-10   Moscow

Confirmed Talks

confirmed talks so far

Diego Hueltes

RavenPack, Spain


Data Science for Lazy People, Automated Machine Learning

Data science is fun, right? Data cleaning, feature selection, feature preprocessing, feature construction, model selection, parameter optimization, model validation – oh wait – are you sure? What about automating 80% of the work even doing better choices than you? Automated Machine Learning has arrived to be your personal assistant in Data Science.

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David Pilato

elastic, France


Managing Your Black Friday Logs

Monitoring an entire application is not a simple task, but with the right tools it is not a hard task either. However, events like Black Friday can push your application to the limit, and even cause crashes. As the system is stressed, it generates a lot more logs, which may crash the monitoring system as well. In this talk I will walk through the best practices when using the Elastic Stack to centralize and monitor your logs. I will also share some tricks to help you with the huge increase of traffic typical in Black Fridays.

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Advanced (Elastic) Search for Your Legacy Application

How do you mix SQL and NoSQL worlds without starting a messy revolution?
This live coding talk will show you how to add Elasticsearch to your legacy application without changing all your current development habits. Your application will have suddenly have advanced search features, all without the need to write complex SQL code!
David will start from a Spring Boot/Postgresql/MySQL based application and will add a complete integration of Elasticsearch, all live from the stage during his presentation.

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Valeriy Babushkin

X5 Retail Group/ Yandex, Russia


How to Increase A/B Convergence Time 10-100 Times

A/B testing is a tricky field especially when the data flow is not enough to converge A/B in a reasonable amount of time. I would love to tell a story how by switching to another most important metric we reduced income traffic 50 times and will tell how we solved this problem using Linearization, Reweighing, Predictive Machine Learning, and Bayes Theorem.

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A/B Testing
Machine Learning

Constant Bridon

OCTO Technology, France


On Board Artificial Intelligence : Train, Deploy and Use Deep Learning on an Edge Device, a Raspberry Pi

Machine learning applications are new in the software development landscape, and tend to be hard to build. As Google noted in an article, it is mainly because the application is much broader than the model itself. Surprisingly though, Machine Learning applications follow a double Pareto’s law. On the one hand, 80% of the time spent on building those applications deals with machine learning problems whereas 20% of the remaining time is spent on…

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Deep Learning

Guglielmo Iozzia

MSD, Ireland


Distributed Deep Learning with Keras and TensorFlow on Apache Spark

DeepLearning4J is an Open Source distributed framework for Deep Learning on the JVM. It allows importing Python (Keras and TensorFlow) models in order to train them in a distributed fashion on Apache Spark. The talk would walk through the reasons for doing distributed Deep Learning of Python models in a JVM based environment and the details to productionalize this process.

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Distributed Deep Learning
Apache Spark

Deep Learning Applied to Failure Management in Apache Spark

During this talk I will walk through a real-world example of AIOps: automated root cause analysis of Apache Spark cluster failures on Kubernetes environments doing Deep Learning with DL4J and… Apache Spark itself.

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Machine Learning

Yulia Stolin

Outbrain, Israel


Realtime Data Pipelines Using Spark Streaming

At Outbrain we serve billions of personalised recommendations.
Our serving ML models were built on top of batch ELT flows.
But having near realtime inputs is extremely important in our business.
During this session, I will present our journey from batch-based to real-time analytics.

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Lambda Architecture
Spark Streaming

Nenad Bozic

SmartCat, Serbia


What It Takes to Build Production Ready AI Solution

We are data company that works with other companies to help them build AI solutions. We are a blend of data scientists and data engineers and that makes us question from different angles how next big AI module will be integrated in your platform. This is prime reason why we can brag that we have more then 10 AI solutions in production developed over the last 3 years.

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AI Solution

Kelly Schlamb

IBM Canada Ltd., Canada


Data Science & AI: Infrastructure Matters

Artificial intelligence is increasingly being seen as a competitive advantage and every company is running fast to try and be a part of this revolution. However, on its own, AI faces a steep time-to-value curve. For example, do you have the right data? Do you have the right skills? Can everyone participate (typically, AI is in the hands of the privileged few and companies struggle to democratize it for the many)? But there is one often overlooked…

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Data Science Infrastructure

Valentina Djordjevic

Things Solver, Serbia


Breaking Out the Lead Scoring Algorithm

The Lead is considered to be any individual who may become a potential client as it has shown an interest in the product or service a company offers.
A Lead Generation refers to the method of collecting leads in order to manage sales channels more efficiently, raise brand awareness and contribute to rising profits. Lead Scoring includes assigning certain weights to each potential…

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Lead Scoring
Data Science

Denis Emelyantsev

McKinsey & Company, Russia


How to Ensure the Work of the Data-Model of a Business

The use of advanced analytics tools and data science models in business comes as no surprise to anybody today. Major companies have long established full-blown departments responsible for big data. Yet not many of them are able to integrate data science models into the everyday processes across the company and achieve really business-wide financial results. In our session we’ll discuss how to go beyond just building the models and achieve significant impact for your business.

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Data Science
Big Data
Financial Effect