Big Data Days 2019

 October 8-10   Moscow

Confirmed Talks

confirmed talks so far

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…

Read more…

Deep Learning
Deployment
On Board
Production

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.

Read more…

Distributed Deep Learning
Apache Spark
DL4J
Java

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.

Read more…

Big Data
Spark Streaming
Kafka
Lambda Architecture

Diego Hueltes

RavenPack, Spain

TALK

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.

Read more…

Machine Learning
Automated Machine Learning

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.

Read more…

AI Solution
Production Ready
Human in the Loop
Success Criteria
Exploratory Analysis

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…

Read more…

Infrastructure
Data Science Power

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…

Read more…

Lead Generation
Lead Scoring
Data Science
Optimization

Olga Petrova

Sencha, Germany

TALK

Visual ML with TensorFlow.js

Advantages of performing machine learning (ML) in browser using TensorFlow.js are not limited to the privacy of user’s data, which is unnecessary for any installations and access to sensors. Another important point is the availability of a rich set of instruments for interactive visualizations and UIs available for JavaScript. This allows us to look inside of the process of model training and re-inforce ML in browsers.

Read more…

Visualization
Tensorflow.js
Machine Learning

Sonya Liberman

Outbrain, Israel

TALK

From Spark to Elasticsearch and Back - Learning Large Scale Models for Content Recommendation

Serving tens of billions of personalized recommendations a day under a latency of 30 milliseconds is a challenge. In this talk I’ll share our algorithmic architecture, including its Spark-based offline layer, and its Elasticsearch-based serving layer, that enable running complex models under difficult scale constrains and shorten the cycle between research and production.

Read more…

Machine Learning
Spark
Elasticsearch
Recommender System

Miel Hostens

Utrecht University, The Netherlands

TALK

Predicting the Moment of Calving in Dairy Cows Using Time Series Analysis in Apache Spark

Stillbirth, defined as calves that die during unobserved birth is often seen as an indicator of lowered animal welfare in dairy cows. Sensors have been proposed as a tool to support dairy farmers but accurate calving prediction models are often lacking. In this session, a machine learning data pipeline will be described using the spark ML framework. Heavy lifting and feature preparation for sensor data from 1331 cows on 8 herds from 21 days before until the day of calving was performed using sliding windows and time series analysis. In total a set of 100+ features was used in a random forest classification model trained and tested using different cross validation approaches.

Read more…