Big Data Days 2019

 October 8-10   Moscow

Confirmed Talks

confirmed talks so far

Diego Hueltes

RavenPack, Spain

KEYNOTE

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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Valeriy Babushkin

X5 Retail Group/ Yandex, Russia

KEYNOTE

Why Data Scientist Does Not Exist

Data Scientist as a job title is a fancy thing. I will tell why there is no such thing as Data Scientist, what is the difference between Data Analyst, Data Engineer, and Machine Learning Engineer. How to enter this field of technology that does not exist?

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TALK

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
Bayes
Linearization
Machine Learning

Constant Bridon

OCTO Technology, France

TALK

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
Raspberry

Guglielmo Iozzia

MSD, Ireland

TALK

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
TensorFlow
Keras

Yulia Stolin

Outbrain, Israel

TALK

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
Kafka

Nenad Bozic

SmartCat, Serbia

TALK

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

TALK

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

TALK

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