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Odessa ,July 21, 2018

Black Sea, July and … Data.
It’s the Data Summer Conf!

It’s the first international Data Summer Conf in Odessa that will host local subject matter leaders and international experts in the data management space.

On the 21st of July Odessa will become a networking stage for data geeks and speakers and let them dig deeper into Data Science & Big Data streams. Join us and share the expertise, insights and trends. Listen to the world data leaders and get real hands-on experience at our data workshops.

Our goal is to get data techies together, build a strong community and have fun here near the Black Sea coast, where the water meets the horizon.

Speakers

15

Participants

400

Agenda

9:00

Registration & Welcome Coffee

9:50

Welcome speech

10:00

Akmal Chaudhri (Technical Evangelist) (BIG DATA & IOT)

Apache Ignite + Apache Spark RDDs and DataFrames integration

Dmitry Korobchenko (Deep Learning R&D Engineer, NVIDIA Ltd) (DATA SCIENCE)

How to accelerate your neural net inference with TensorRT

Fedor Navruzov (Data Scientist, Speak With a Geek) (WORKSHOPS)

Pandas Crash Course

11:00

Jacek Laskowski (Spark Consultant ) (BIG DATA & IOT)

Deep Dive into Query Execution in Spark SQL 2.3

Javier Rodriguez Zaurin (Data Scientist, Simply Business) (DATA SCIENCE)

From the math to the business value: machine learning in the real world

12:00

Coffee break

12:30

TBD (BIG DATA & IOT)

 

Giuseppe Angelo Porcelli (Solutions Architect at Amazon Web Services)(DATA SCIENCE)

Build, train, and deploy machine learning models at scale with Amazon SageMaker

Akmal Chaudhri (Technical Evangelist) (WORKSHOPS)

Hands-on with Apache Spark for Beginners

13:30

Rudradeb Mitra (Product Mentor, Google Developers) (BIG DATA & IOT)

Architecting IoT system with Machine Learning

Sri Sri (Sr. Data Scientist, Spotify) (DATA SCIENCE)

Multi-touch Attribution: Key challenge around designing a solution

14:30

Lunch

15:30

Giorgi Jvaridze (Software Engineer, Zalando) (BIG DATA & IOT)

Analysing Billion Node Graphs

TBD (DATA SCIENCE)

 

Jonathan Taws (Data Scientist at Amazon Web Services) (WORKSHOPS)

Hands-on using Apache Spark with Amazon SageMaker

16:30

TBD (BIG DATA & IOT)

 

Roman Storchak (CTO, DatAI ) (DATA SCIENCE)

How we build Computer vision as a service

17:30

Panel Discussion

18:30

Conference Closing

19:00

Afterparty (True Man Hot Boat bar )

19:30

BIG DATA & IOT

DATA SCIENCE

WORKSHOPS

9:00

Registration & Welcome Coffee

9:50

Welcome speech

10:00

Akmal Chaudhri (Technical Evangelist)

Apache Ignite + Apache Spark RDDs and DataFrames integration

This session will explain how Apache Spark and Ignite are integrated, and how they are used to together for analytics, stream processing and machine learning. By the end of this session attendees will understand: – How Apache Ignite’s native RDD and new native DataFrame APIs work – How to use Ignite as an in-memory database and massively parallel processing (MPP) style collocated processing for preparing and managing data for Spark – How to leverage Ignite to easily share state across Spark jobs using mutable RDDs and DataFrames – How to leverage Ignite distributed SQL and advanced indexing in memory to improve SQL performance

11:00

Jacek Laskowski (Spark Consultant )

Deep Dive into Query Execution in Spark SQL 2.3

Dmitry Korobchenko (Deep Learning R&D Engineer, NVIDIA Ltd)

How to accelerate your neural net inference with TensorRT

Modern neural networks are based on high-load computing. Both hardware and software are important for fast training and inference. Modern high-level frameworks, used to build and train a neural net, can sacrifice performance in favor of greater flexibility. Therefore, for deployment of a trained neural net in production an optimization of the performance is required. In the talk I will demonstrate possibility of such optimization and further fast inference on GPU using TensorRT.

Javier Rodriguez Zaurin (Data Scientist, Simply Business)

From the math to the business value: machine learning in the real world

I will illustrate the “life-cycle” of a machine learning project, particularly the building of a recommender system, from the technical design (business problem and detailed algorithmic solution) to deployment. I will also briefly mentioned other examples (e.g. marketing multi-channel attribution models using Markov-Chain models, or boosted methods and deep learning for risk modelling) trying to emphasise the journey from the code to the business with a couple of histories of success and failure.

Fedor Navruzov (Data Scientist, Speak With a Geek)

Pandas Crash Course

The topics, covered within workshop, are main pandas objects, ways of loading data, diverse manipulations / filterings / transformations / groupings and aggregations of data. The participants will also get hangs on experience with handling special types of data (textual, datetime-based, missing values). A separate topic would cover inline and external methods of data analysis and visualization (pandas, pandas-profiling). The target audience are junior-to-middle data scientists, as well as participants who want to update / to deepen their knowledge of pandas.

12:00

Coffee break

12:30

TBD

13:30

Rudradeb Mitra (Product Mentor, Google Developers)

Architecting IoT system with Machine Learning

In this talk, the speaker will share his experiences from building successful IoT systems. He will also explain why many IoT systems fail to get traction and how Machine Learning can help in that. Finally, he will talk about the right system architecture and touch upon some of the ML algorithms for IoT systems.

Giuseppe Angelo Porcelli (Solutions Architect at Amazon Web Services)

Build, train, and deploy machine learning models at scale with Amazon SageMaker

Machine learning often feels a lot harder than it should be to most developers because the process to build and train models, and then deploy them into production is too complicated and too slow. Amazon SageMaker is a fully-managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Apache MXNet and TensorFlow are pre-installed, and Amazon SageMaker offers a range of built-in, high-performance machine learning algorithms. If you want to train with an alternative framework or algorithm, you can bring your own in a Docker container.

Sri Sri (Sr. Data Scientist, Spotify)

Multi-touch Attribution: Key challenge around designing a solution

When I worked with Skyscanner, before Spotify, I worked on a popular marketing problem called Multi-touch Attribution. In this talk, I’ll explain Multi-touch attribution problem space and how a Data Science solution was designed for this problem.I focus on a key challenge of designing a Multi-touch attribution solution – Identifying which methodology is best for this problem. This is a thorny issue as the variable we are trying to study isn’t directly observable. I’ll discuss a novel approach to tackle this problem through simulated marketing environment.

Akmal Chaudhri (Technical Evangelist)

Hands-on with Apache Spark for Beginners

In this workshop, attendees will use Apache Spark to undertake some simple calculations and solve some data manipulation problems. Through Python programming exercises, attendees will be able to get some hands-on experience with Spark using a cloud-based environment. The goal is to show the power of Spark, without needing to understand its complexity. Preparation for workshop: Detailed instructions will be provided closer to the event. However, essentially, attendees would need to create a free Databricks Community Edition (CE) account and bring their laptop with them. The workshop would require good wifi to connect to CE, hosted in the USA.

14:30

Lunch

15:30

Giorgi Jvaridze (Software Engineer, Zalando)

Analysing Billion Node Graphs

Many important data science problems can be approached using graphs. The data that can be represented as graphs are everywhere: Social networks, Economic networks, Biomedical networks, Network of Neurons, the Internet itself. Some of those graphs can become very large. There are many challenges that we have to deal with when we want to process, analyse and visualize large graphs. In his talk Giorgi Jvaridze will talk about experiences he had working with multi-billion node graphs.

16:30

TBD

TBD

Roman Storchak (CTO, DatAI )

How we build Computer vision as a service

During this speech, we will look at the different versions of SaaS architectures built on the basis of ML / computer vision: – The advantages and disadvantages of using different design patterns of services – Modes of “serving” models (in most cases, TF) – Influence of architecture and the way it is implemented on product development. Bonus: Does the data scientist (y) need to know something other than data science?

Jonathan Taws (Data Scientist at Amazon Web Services)

Hands-on using Apache Spark with Amazon SageMaker

Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker also provides an Apache Spark library, in both Python and Scala, that you can use to easily train models in Amazon SageMaker from your Spark clusters. Once a model has been trained, you can also deploy it using Amazon SageMaker hosting services. After a brief recap on Amazon SageMaker, this code-level workshop will show you how to integrate your Apache Spark application with Amazon SageMaker, including how to start training jobs from Spark, integrating them in Spark pipelines, and more.

17:30

Panel Discussion

18:30

Conference Closing

19:00

Afterparty (True Man Hot Boat bar )

19:30

Speakers

Please meet our first international guest speakers!
We work hard to add more star speakers to our pipeline.
In the coming weeks see our full agenda and finalised workshops program.
Stay tuned!

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Sameer Farooqui

Strategic Cloud Engineer for Big Data

The fields of Big Data and Artificial Intelligence will bring stunning changes to all aspects of human civilization in the coming years and the impact will likely be more profound than the internet or mobile phones. Data Summer Conf in Odessa is a great way to learn from experts who are at the cutting edge of these fields.

Misa Ogura

Software Engineer

WiDS London Ambassador

I'm excited about Data Summer as an international initiative to reflect the current state of Data Science / Big Data. I look forward to seeing it drive better Diversity & Inclusion in the field and facilitate wider collaborations!

Yana (Ponomarova) Gonnord

Enterprise Data Scientist

Data Summer sounds like an amazing initiative. I find the focus on technical implementations and real-life experiences increasingly beneficial and hope, it will serve as a basis of rich exchanges and collaborations

Paul Brook

Director Data Analytics EMEA

Data Summer is a premier event. You wait for the date and if you cannot go you get disappointed and then wait for the feedback and the recordings. Premier events are always best live. Ask Questions. Network and meet friends old and new.

In good company

Industry-defining brands that have helped make
Data Summer Conference:

Tickets move fast

What does my ticket include?

  • Conference Welcome package
  • Access to tech talks on Data Science stream
  • Access to tech talks on BIG DATA & IOT stream
  • Access to Workshops
  • Lunch and Сoffee Breaks
  • Visiting afterparty
  • Video recordings of tech talks + Presentations

Every ticket helps mentor a child

Your contribution goes to helping kids get a better start in life at Atom Space.

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Ukraine, Odessa

Sady Pobedy

vul. Varlamova, 28

datasummer@provectus.com

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