Our first speakers

Conference Day (Feb 22)
Andrew is a long-time Django contributor - author of South, Django Migrations, and Channels - and a Principal Engineer at Eventbrite, working on SRE and systems architecture.

He has been doing backend programming for over 15 years, and Python for over a decade, and when he's not coding, you can find him flying small planes, playing around with electronics, and visiting every National Park he can.
Sebastián is the creator of FastAPI, a modern web API Python framework, and some other Open Source projects.

He has worked with different teams in many projects, from Latin America to the Middle East.

He does lots of Open Source, APIs, Python, JavaScript (TypeScript), Docker, DBs, ML, etc.
You know Machine Learning, your models are working well, the team likes the results… but now you need to "serve" them in an API so that others can interact with it (developers/frontend team/other code), for production.

In this talk, you will learn how to easily build a production-ready web (JSON) API for your ML models with FastAPI, including best practices by default. With very little code, you will get automatic/interactive documentation, data validation, authentication, and more.

On top of that, you will have autocomplete and type checks in your editor, even for your own data, no matter how complex its shape is.

The talk is targeted at Machine Learning practitioners who don't know web development yet but can be appropriate for anyone interested in building web APIs.
Software engineer at Amazon Web Services, Amsterdam.

Born and raised in Minsk but moved to the Netherlands to build cloud-based IDE - AWS Cloud9.
This talk will explain in details how modern IDEs and code editors provide language support, in particular for Python, what tools and protocols are used to make it possible.

It touches upon such topics as:
• Overview of Python IDEs
• Language features and LSP protocol
• Detailed look at tools used for Python language features
• Live demo of these tools

This talk can be interesting for anyone who is curious how IDE works in the context of Python.
Ronan Lamy has been a Python consultant and open-source developer for more than 10 years.

Born in France, he now lives in Bristol, UK. He's been a core developer of PyPy since 2012, with particular focus on Python 3 features, C-API emulation and the data science ecosystem.
Performance is a major concern for Python programmers. With the standard interpreter, CPython, performance-sensitive code needs to be rewritten in a faster, but less convenient, language such as C or Cython.

However, there is another implementation of the Python language: PyPy. It's a Python interpreter that features just-in-time compilation of Python code to machine code and efficient memory management. It supports Python 2.7 and 3.6 and most C extensions, including e.g numpy, Cython, scipy, pandas, so it can often be a drop-in replacement for CPython.

With PyPy, there is no need to choose between clear, Pythonic code and good performance.
Developer at Yandex.Weather.

Lena conducts experiments to improve weather forecasting with machine learning. Also she supports calculation of production models.
Numerical weather prediction models use all mankind's knowledge of atmospheric physics, a variety of data sources and the computation power of supercomputers. But still they are not absolutely correct.

I will describe how Yandex.Weather improves forecasts of physical models with CatBoost and other machine learning algorithms.

Besides that I will show the pipeline of nowcasting, which uses neural networks, geostationary satellite imagery and meteorological radars measurements to provide precipitation maps with temporal resolution up to 10 minutes.
Loves to scrutinize the disassembled code, work with metaprogramming and play with AST.

For the last 3 years Iuliia has been working as a backend developer as well as a BigData engineer in various projects.

In the spare time she writes some articles and tutorials (mostly about Apache Airflow) on Medium and tries to grow and contribute to Python communities inside the company and in Saint-Petersburg.
After suffering through several legacy-projects that had been thrown to me without any unit tests, I got a maniac idea that had been on my mind for a long time. "Can I implement some program that will make the same steps like I would, when covering legacy code with unit tests?" I want to share with you the experience I acquired while exploring this idea.

That will include Abstract Syntax Trees (AST), code generation, overview of different related tools and libs that exist in Python, little bit about tokenization, how all of that help me as a developer and of course we will take a look at different examples and use-cases from the actual results that I already have.
Roman is a machine learning engineer at Flo Health and Kaggle Competitions Master.

He has experience in constructing full-cycle ML solutions starting from problem statement and translating business needs to ML language ranging to implementing and delivering models to production.

Most of experience lies in domain of social networks analysis, natural language processing and business applications of machine learning. Also passionate about data science competitions and knowledge sharing.
In this talk I'll cover a full process of solving a real-world problem using machine learning.

How to decide whether it is a time to move from expert systems to ML, which prerequisites should be met before running ML initiative, which typical problems usually arise here.

Also I'll describe several ways to organize ml experiments in a proper way and setup reproducible training pipeline. Notes on models' artifacts managing will be made, and finally we'll talk about ways to deliver ML models.

This talk can be interesting for data scientists and ml engineers or for anyone who participates in a process of building ML \ DS culture in a company.
Kirill is a data scientist and Python-dev from St. Petersburg, Russia. He has a master's degree in Data Analysis. Worked as a freelance Python-dev and data scientist for 1.5 years. Now he is a data scientist at SEMrush.

Kirill is interested in a problem of experiments' reproducibility in ML and product management for ML-based products. Sometimes gives public talks related to these topics. He was a speaker at conferences "Big Data Days 2019" and "PiterPy2019" and at meetups.
In the 21st century, the era or "rock stars" in software development has come to an end. Now almost every good project is backed by a team of developers relying on the best practices evolved over the years of IT sector growth. These practices, such as using a single repository, storing a codebase under a version control system, code coverage with tests, defining code style conventions, help good engineers to collaborate effectively and create high-quality products.

Unfortunately, it is not so easy to adopt the best practices of software development for data analysis. To do so, we need to find the tools and approaches that will take into account the special aspects of ML projects: working with big datasets, numerous pipelines and a huge number of models with many hyperparameters.

I will tell you how to facilitate the interaction between data scientists, speed up and standardize the process of conducting experiments and achieve a reproducibility of the results of these experiments.
Anna is a Data Scientist at IBM in Sweden currently working with IoT and Smart Cities project for Stockholm City.

Her career started over 15 years ago in Russia within marketing & sales went on into finance & entrepreneurship in Sweden and, finally, into IT & data analysis. Her interdisciplinary experience lets her approach data science problems from different angles. She sees beyond mathematical models and code into what the end-user actually needs and how it influences other people involved in the data.

Anna is active in the local tech community and encourages people of different backgrounds to take the same leap into technology as she once did, making the field more diverse and inclusive.

Our first workshop

Workshop Day (Feb 21)
Sebastián is the creator of FastAPI, a modern web API Python framework, and some other Open Source projects.

He has worked with different teams in many projects, from Latin America to the Middle East.

He does lots of Open Source, APIs, Python, JavaScript (TypeScript), Docker, DBs, ML, etc.
You know at least the basics of Python (or a ton of it) and want to do "backend development" and build "the cloud". Now what?

In this workshop, we'll build a web (JSON) API app from scratch using FastAPI.
You will learn some current best practices from the beginning and will have a production-ready web API by the end, served by a local database on your computer.

Your API will have automatic/interactive documentation that your coworkers (or the frontend team) will love… as well as your future self. It will have automatic data validation (including detailed data validation errors), integrated authentication, etc. You will also learn some of the advanced techniques (like dependencies) that will save you a lot of time developing. All of this, with a development experience based on editor autocomplete and type error checks everywhere.

The workshop targets beginner-level developers, but experienced developers in other frameworks that want to check FastAPI are also welcome.

Please stay tuned

More speakers to be announced soon. Meanwhile, follow us for the updates!
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Ticket sALES SCHEDULE

until February 10
REGULAR
90 BYN
WORKSHOP DAY
(FEB 21)
225 BYN
CONFERENCE DAY
(FEB 22)
270 BYN
COMBO TICKET (FEB 21+22)
February 11 onwards
LAST MINUTE
130 BYN
WORKSHOP DAY
(FEB 21)
290 BYN
CONFERENCE DAY
(FEB 22)
350 BYN
COMBO TICKET (FEB 21+22)
until February 10
REGULAR
90 BYN
WORKSHOP DAY
(FEB 21)
225 BYN
CONFERENCE DAY
(FEB 22)
270 BYN
COMBO TICKET (FEB 21+22)
February 11 onwards
LAST MINUTE
130 BYN
WORKSHOP DAY
(FEB 21)
290 BYN
CONFERENCE DAY
(FEB 22)
350 BYN
COMBO TICKET (FEB 21+22)
99 EUR / 110 USD / 6991 RUB
39 EUR / 44 USD / 2796 RUB
118 EUR / 132 USD / 8389 RUB
127 EUR / 141 USD / 9010 RUB
57 EUR / 63 USD / 4039 RUB
154 EUR / 171 USD / 10875 RUB
until February 10

Some pictures from the last conference

See 273 more on our Facebook page

How it felt over the last years

PyCon Belarus 2018
PyCon Belarus 2019
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Lasse founded a software product and a software service company and has consulted many companies on how to launch their products fast.

With his technical background in addition to the practical business experience, he looks not only at the technicalities but also at their business implication to reach actual goals instead of ticking off a specification.

In his spare time, Lasse likes to conduct talks and workshops for clients and at conferences all around the world.
Almost all developers have many ideas waiting to be implemented. Tons of them. However, we oftentimes struggle even turning very few of them into software that is not only well usable but also used well.

That's because hitting the market with a new idea is hard. Even bigger companies and corporations oftentimes take years to develop an initial version of their product just to figure out that much of what they did is not what the user needed.

The MVP (Minimum Viable Product) philosophy helps avoiding those pitfalls by starting a product with the minimum possible features and incrementally launching a product to more and more people. With the right strategy, one can launch an initial product within 30 days instead of 6-12 months with a much smaller budget.
Alejandro is the Chief Scientist at the Institute for Ethical AI & Machine Learning, where he leads highly technical research on machine learning explainability, bias evaluation, reproducibility and responsible design.

With over 10 years of software development experience, Alejandro has held technical leadership positions across hyper-growth scale-ups and tech giants including Eigen Tchnologies, Bloomberg LP and Hack Partners.

He has a strong track record building departments of machine learning engineers from scratch, and leading the delivery of large-scale machine learning system across the financial, insurance, legal, transport, manufacturing and construction sectors (in Europe, US and Latin America).
In this talk we will demystify the concept of "bias in machine learning" through a hands-on example. We will be tasked to automate an end-to-end loan approval process by training a deep learning tensorflow model on sample data. We will then dissect the model and dataset showcasing hidden common risks, and we will provide important insight on the key tools and techniques you can use to identify and mitigate undesired biases.

Throughout this talk we will cover fundamental concepts such as class imbalance and feature importance, as well as key tools such as SHAP, LIME, and more.
Ewa Jodlowska is the PSF's Executive Director. She has been with the Python Software Foundation since 2012 and prior to that she assisted with PyCon as a third party contractor.

Ewa's responsibilities include giving direction and leadership to the Foundation, working with the board of directors on long-range strategic planning, and overseeing financial and program operations to name a few.
Picture your favorite vacation destination. There's probably a community that is unique to it and elements of this community that make it welcoming. If Python had it's own island that we'd all go visit from time to time, how would we want that community to look?

Python's popularity is global and every community within Python's ecosystem is unique. But, we all share a major common interest! In this talk we will discuss core elements that are essential to the sustainability of Python and it's community. This talk will also highlight the PSF's involvement in the Python community and we'll discuss updates in Python governance.
Martin Christen is a professor of Geoinformatics and Computer Graphics at the Institute of Geomatics Engineering at the University of Applied Sciences Northwestern Switzerland (FHNW). His main research interests are geospatial Virtual- and Augmented Reality, 3D geoinformation, and interactive 3D maps.

Martin Christen is very active in the Python community. He teaches various Python-related courses and uses Python in most research projects. He organizes the PyBasel meet up - the local Python User Group Northwestern Switzerland. He also organizes the yearly GeoPython conference. He is a board member of the Python Software Verband e.V. and the EuroPython Society.
Geospatial data is data containing a spatial component – describing objects with a reference to the planet's surface. This data usually consists of a spatial component, of various attributes, and sometimes of a time reference (where, what, and when). Efficient processing and visualization of small to large-scale spatial data is a challenging task.

This talk describes how to process and visualize geospatial vector and raster data using Python and the Jupyter Notebook.
There are numerous modules available which help using geospatial data in using low- and high-level interfaces. We will look at shapely, which is used for manipulation and analysis of geometric objects.
Then we go further to Fiona – a module which handles geospatial vector data in a very pythonic way. We move on to raster data processing using the rasterio module and briefly look at the pyproj module which is used for transforming spatial reference systems.
After that we look at GeoPandas which is basically an extended pandas module with support for geodata.
At the end we will see how maps are created using the cartopy and folium modules.

At the end of the talk some examples are shown how to use deep learning for raster analysis using a GPU cluster.
Tania is a Research Engineer with vast experience in academic research and industrial environments. Her main areas of expertise are within data-intensive applications, scientific computing, and machine learning. One of her main areas of expertise is the improvement of processes, reproducibility and transparency in research, data science and artificial intelligence.

Over the last few years she has trained hundreds of people on scientific computing reproducible workflows and ML models testing, monitoring and scaling and delivered talks on the topic worldwide.

She is passionate about mentoring, open source, and its community and is involved in a number of initiatives aimed to build more diverse and inclusive communities.

She is also a contributor, maintainer, and developer of a number of open source projects and the Founder of Pyladies NorthWest UK.
This talk will examine with critical eyes the uses, advantages and disadvantages of the Jupyter notebooks. It will, for this purpose, take the audience into a journey across some of the best tools and lesser-known features of the notebooks (examples included). Later on, we will explore some of the pain points that come with notebooks and cases for which Jupyter notebooks are not the best tool to do the job.
Chief maintainer of Luigi. Previously working with Data Infrastructure at Spotify and VNG but now working with frontend and backend at YouTube (Google).
Luigi is a Task Orchestration tool from Spotify that was open sourced in 2012. The purpose of Luigi is to address all the plumbing typically associated with long-running batch processes.

This talk will introduce luigi and look at how it helps you follow best data pipeline practices. We will also look at how Luigi can be used to separate scheduling from execution.
As a current member of the Core Infrastructure team at Booking.com, Kirill has more than 12 years of Python experience, most of which has been devoted to preaching good coding practices. From monolithic accounting systems to authentication microservices, it has been a challenging ride littered with the rotting husks of legacy codebases.

That journey has also allowed him to accumulate a wealth of experience, many colorful anecdotes and some very sound advice, which he has shared previously at various conferences and meet-ups. There is, of course, always more to learn and to share.
Many programmers agree that tests are a must-have in any codebase that needs refactoring. Many programmers also agree that this problem is hard to tackle or have no idea where to start. This talk aims to share our experiences of improving 10-year legacy codebase by introducing tests, tests & tests.

In this talk, you'll hopefully learn something about the following things:
• How to broach the subject with your manager and not get laughed at
• Possible avenues of attack on the beast itself
• Common pitfalls that await those who dare to push through with this
• Tips and tricks that may (or may not come in handy)
Hynek Schlawack is a lead infrastructure and software engineer from Berlin, a PSF fellow, a maintainer of too many open source projects, and a contributor to even more.
His main areas of interest are networks, security, and robust software.
The DevOps movement gave us many ways to put Python applications into production. But should your application care? Should it need to know whether it's running on your notebook, on a server, in a Docker container, or in some cloud platform as a service? It should not, because environment-agnostic applications are easier to test, easier to deploy, easier to handle, and easier to scale.

But how can you practically structure and configure your applications to make them indifferent to the environment they run in? How do secrets fit into the picture? And where do you put that log file?

By the end of this talk you'll know the tools and techniques that enable you to write such Python applications and you'll be ready for the next big change.
Grigory Petrov, 20 years in software development. He is one of developers behind Radmin and DevRel for such projects as NPTV and Voximplant.

Jack-of-all trades, helps organize Python and JavaScript events in Moscow. Loves Ruby, but never uses it in production. On a sabbatical leave right now.
Talk 'What are variables': It is natural for people to explain new things through already known ones, using analogies and building knowledge on the foundations of existing ones. A good explanation of the variables, I have not yet met. All I have seen is attempts to explain variables through themselves or by drawing analogies with mathematics. In my speech, I will try to explain the variables:
• without telling in advance how the computer, memory and compiler work;
• without introducing a bag of additional entities like "assignment", "data", "operator" and the untranslatable "evaluate";
• not drawing analogies with mathematics.
Talk 'How to learn to read any code': As simply as possible I will talk about how the program text looks in any programming language from the point of view of the programming language itself. You will learn about expressions and statements, about the terrible thing to evaluate, about the fact that you will have to learn English, and many other interesting things.
Valentin Malykh has experience in information retrieval (Sputnik & Yandex), dialog systems (iPavlov & VK).

Now he is working on research in natural language understanding.
In this workshop you'll get into chatbot creation and more.

There are different parts of the library which could be heplful far behind pure dialog systems, like named entity recognition component or open domain question answering. On the one hand our library provides you with tools to simplify development of complex models, so you just need to get data in right format.

In this workshop we will learn how to prepare data for a model. On the other hand our library already has a lot of pre-trained models which could be used and we teach you how to use these building blocks for your own dialog system.

Come to our workshop and find out what could be useful for you!
Have a 9+ years' experience in web development.

Working on the merchant line:
• merchant sites engine
• app store and design templates store
• CMS for sites, marketplaces and external platforms
• payment and shipping services
• mobile apps development.

GraphQL enthusiast.
GraphQL is a new black, a hype over the Internet with a very few real-life examples of how to use it in big in-house projects. I'd like to show the real example of GraphQL Evolution from a small mobile API to a cross-services integration in a high-load Python project that took us 3 years to develop.

Intro to GraphQL in the Python world.

Step-by-step GraphQL evolution in a big high-load python project:
Step 1. Mobile App API with GraphQL
Step 2. Separate Frontend from Backend in high-load python project using GraphQL
Step 3. Graph services as Proxy via different Graph APIs
Step 4. Replace SqlAlchemy models via Graph
Step 5. Mutations in Graph API
Step 6. A brave new world with GraphQL

For every step, I will provide real examples (metrics, graphics, numbers), problems and solutions that we had during the years 2015 - 2019.
Yuriy Guts is a Machine Learning Engineer at DataRobot with over 10 years of industry experience in data science and software architecture.

His primary interests are productionalizing data science, automated machine learning, time series and multiseries forecasting, processing spoken and written language.

He teaches AI and ML at UCU, and has led multiple data science and engineering teams in Ukraine.
He is also a Kaggle Competitions Expert in the top 2% worldwide.
Reinforcement Learning is a hot AI topic in industry and academia, as it involves teaching a computer to perform complex tasks just by trial and error, without understanding how the world works. The best human players in Go and Texas Hold'em Poker have been beaten by RL-powered AI recently.

In this session, we'll take a look at what Reinforcement Learning is, and use it to make our computer an expert player of a computer game just using open-source Python tools and commodity hardware.
Anna Veronika graduated from the Faculty of Computational Mathematics and Cybernetics of Lomonosov Moscow State University and Yandex School of Data Analysis.

She used to work at ABBYY, Microsoft, Bing and Google, and has been working at Yandex since 2015. Deals with tasks related to the development of machine learning algorithms. Leads the team which develops the CatBoost library.
CatBoost by Yandex is a library of gradient boosting. It is publicly available.
The main features of this library: it allows you to work effectively with categorical data, gives increased accuracy due to methods of dealing with retraining, realizes the ability to count model values for time-critical services quickly, and also gives the opportunity to train models on big dataa.

In the talk we will explain what gradient boasting is and why it is needed, will highlight the main features of the library and tell you what it is important to know and use while training gradient boosting models.
Research fellow at the International science laboratory of intelligence systems and structural analysis at Higher School of Economics.

Artem does reaserch of professional education, build self-acting methods of ontological education with design on an employment market. He works on feature engineering / extracting, data mining and data visualization projects.
Have you ever made a decision of data visualization? It's the best way for understanding your process and explain something for your customers. What do you prefer: use an existing platform or build yours? You'd better build yours!

I'm going to tell about advantages of making a personal dashboard due to python jointly with Plotly / Bokeh. In speech ETL process will be observed as a process for supporting relevance of data and how python could be involved on it. As a result you will have lots of arguments for making your platform and you will know which skills could be pumped during the work. Plenty of examples of right statistical visualization are going to be passed through the report for afters.
Co-founder of learn.python.ru.

Ilya programs, designs, manages and teaches.
The workshop will be useful to junior Django developers. We will write a project from scratch, will use the necessary libraries, will optimize work with the database.

As a result, we'll get a ready-made application with business logic that can be developed and maintained.

In practice, we will get acquainted with the necessary techniques of commercial development on Django.
Data scientist & engineer experienced in software architecture, consulting and leading teams with solid technical background.
Main areas of competencies are machine learning in general, data engineering, business intelligence, natural language processing, computer vision and building development processes in data science projects.

In the field of data analysis since 2012. Active member of Belarusian IT communities and mentor of hackathons.
This workshop will be useful for everyone who familiar with Python and wants to quickly dive into working with data analysis in their own projects.

We will start with an introduction to data analysis and try to go through the entire pipeline from setting an analytical problem to the final result.
Ivan has walked his way from IT Support to Software Developer.

He knows very well how hard is to learn something new and how easy is to kill someone's motivation to learn.
Ivan has been a python programmer for the last 7 years. Used to work with Yandex, Wargaming, and others.

He believes that our civilization depends on qualified (i.e. experienced) software developers. So, their education (i.e. experience gain via practice) is the best investment. So, he is doing internal education and mentoring for a year now.
These days, everyone is using some helper-bots from time to time. Those bots often allow automating some business processes (for its creator for sure). If you haven't built one yet and don't know how it should work, but have an idea of what to automate, we definitely should meet.

In this workshop, we will:
• Discuss the lifecycle of the bot;
• Separate the transportation guts (integrations with various APIs) from the machine's brain (the business logic to make decisions);
• Perform small steps with frequent testing toward the "walking skeleton" solution which will be open for extension and ready for usage;
• Have I told you about the paired programming? Yes, we may do this as well!
Currently developing web applications in telematics. Previously worked with financial data analysis and numerical modelling of physical processes.
MQTT is a publish/subscribe messaging transport protocol. It is open, light-weight and simple. These characteristics make it ideal for use for communication in Machine to Machine (M2M) and Internet of Things (IoT) contexts. Python is also extremely popular in IoT world. So we have a great Python + MQTT combo.

In this talk Elena will introduce MQTT protocol and its main features. We'll discuss what's new in latest MQTT 5.0 version and why is it cool. Elena will also show some interesting usage examples, their experience and compare main brokers and clients implementations available right now (mainly concentrating on those implemented in Python of course).
Passionate engineer, loves to code and solve complex issues.

Now he is a part of the backend team at Flo Health Inc., where he spends his time creating the best service on women's health.
If you use Python, you most probably use virtual environments and pip to install packages. You may have requirements.txt with all dependencies; you may even have two of them, for example, requirements-dev.txt. But what if I tell you that this good old approach has apparent problems, and there is more than one instrument that tries to solve these issues?

My speech addresses the existing problems in managing dependencies. I will tell you as well on how the developers tried and still try to solve these issues, and we will take a closer look at a set of tools: pip-tools, pipenv, flit, poetry. Together we will decide if these instruments worth a shot, or is it just a trivial train of madness it's better to avoid.
Senior Software Engineer
Full Stack Web Developer
Open Source Enthusiast
Hackathons Fan
Public Speaking Amateur ;-)
In this talk I'll show how you might manage dependencies more painless and in a modern manner. In most tutorials you can see that you just need to use pip and requirements.txt and this is fine. It's true… as long as you're doing it on your local machine, without production server, without teammates. If you're developing some production-ready project in a collaboration with a team you need some another approach.

I will show how you can move your local «pet» project from pip+requirements.txt to Pipenv, a brand-new system for a package managing in the Python world. In addition, I'll highlight why all this is necessary.

A BONUS PART: we will learn how you can manage dependencies in projects using conda, the special tool for the special Python platform — Anaconda which is mainly popular in a Data Science and Machine Learning field.

Programme committee

Passionate Software Engineer
Software Engineer @Flo Health
Software Engineer
Computer Vision Engineer @Mapbox
Director of Engineering @DataRobot
Senior Software Engineer @Astronomer
PyCharm Techincal Lead
Machine Learning Engineer
Nikolay has been in web development for more than 12 years and for about 6 of them he is falling into Python.

Active participant of Minsk Python community.
Passionate engineer, loves to code and solve complex issues.

Now he is a part of the backend team at Flo Health Inc., where he spends his time creating the best service on women's health.
Python developer at day time, Go developer (gopher) under the hood. Big fan of full-text search and graph databases.

Contributed to different python/go open source projects:
- pyhelm, aiohttp-swagger, mezzanine
- chalice, requests, aiohttp tutorial
- sendgrid-python and sendgrid-django
- OpenAPI v3 specification, fix Go docs

Speaker at PyCaribbean, PyCon Italia 2017, EuroPython 2016, PyCon Ukraine 2014, PyCon Belarus 2015-2018 PyCon Russia 2015, 2016.
Blogger at https://asoldatenko.com/


Passionate software engineer. Digital nomad. Python developer.

Area of interests: high load applications.
PyCharm Technical Lead and IdeaVim maintainer at JetBrains.

My interests: Python, Kotlin, static code analysis, electronic music, history, avant-garde art.
I teach computers to see, hear and understand.

Hate all software but develop it anyway.

Create maps from images. Kaggle Master and active contributor to Open Data Science community.
Machine Learning Engineer at a secret startup. Prior to that was engaged in machine learning at WANNABY, Juno, Yandex, worked as a product manager at Wargaming.

Co-organizer of Data Fest Belarus.
Python and Rust enthusiast from Minsk, Belarus.

Passionate about communities, artificial intelligence and development.

Now building the world's most advanced Enterprise AI platform at DataRobot.

Become a Partner

Your Partner Account Manager is Misha Malikin:
+375 29 678-56-34, misha@eventspace.by

Previous years' Partners

Participant Partner

General Media Partner

Media Partners

Welcome to Belarus

About Belarus

Belarus has a strong IT cluster of international companies. It is worth to mention EPAM, World of Tanks, Fitbit, PandaDoc, MSQRD, Juno, etc.

30 days visa-free

About Minsk

Minsk is the 11th most populous city in Europe. It is a very safe and green city with great cuisine.

Hotel Discount

If you need a hotel, after purchasing a conference ticket, contact the organizers and get a discount on Willing hotel.
If you fly to Minsk airport from any country except Russia & your stay will last up to 30 days (including arrival & departure dates), the visa will be stamped to you free of charge at Minsk airport!

This concerns 74 countries' citizens.

If your country is in the list, you don't need an invitation to enter the country. You'll only need a valid passport (it must be valid 6 months after your trip to Belarus), a return ticket and medical insurance that must be purchased at Minsk airport upon arrival (before passport control), it costs a couple of euros, the insurances from your countries might be not valid for our passport control.

If your country is not in this list, we can prepare an invitation for you.

ASK QUESTIONS:

Valentina Fedortsova – Content and organization
valentina@eventspace.by
+375 33 667-66-03

SPACE PORTFOLIO:

Misha Malikin – Partnership and corporate tickets
misha@eventspace.by
+375 (29) 678-56-34
PyCon Belarus 2020 Code of Conduct
All attendees, speakers, sponsors and volunteers at our conference are required to agree with the following code of conduct (CoC). Organisers will enforce this code throughout the event. We are expecting cooperation from all participants to help ensuring a safe environment for everybody.

PyCon Belarus 2020 is a community conference intended for networking and experience exchange in the developers community.

PyCon Belarus 2020 is dedicated to providing a harassment-free conference experience for everyone, regardless of gender, sexual orientation, disability, physical appearance, body size, race, or religion. We do not tolerate harassment, discrimination, abasement and any form of disrespect.Sexual language and imagery is not appropriate for any conference venue, including talks.

We urge to avoid offensive communication related to gender, sexual orientation, disability, physical appearance, body size, race, religion, sexual images in public spaces, deliberate intimidation, stalking, following, harassing photography or recording, sustained disruption of talks or other events, inappropriate physical contact. Attending the event under the influence of alcohol or other narcotic substances is unacceptable.

Exhibitors in the expo hall, sponsor or vendor booths, or similar activities are also subject to the anti-harassment policy. In particular, exhibitors should not use sexualized images, activities, or other material. Booth staff (including volunteers) should not use sexualized clothing/uniforms/costumes, or otherwise create a sexualized environment.

Conference participants violating these rules may be sanctioned or expelled from the conference without a refund at the discretion of the conference organizers.

Expected Behavior
  • Participate in an authentic and active way.
  • Exercise consideration and respect in your speech and actions.
  • Attempt collaboration before conflict.
  • Refrain from demeaning, discriminatory, or harassing behavior and speech.
  • Be mindful of your surroundings and of your fellow participants. Alert organisers if you notice a dangerous situation, someone in distress, or violations of this CoC.
Thank you for helping make this a welcoming, friendly event for all!

Need Help?
Contact the organizer at valentina@eventspace.by.
Corporate tickets booking
Prices are inclusive of all taxes
Conference Day (Feb 22)
Regular Ticket @225 BYN
+
Workshop Day (Feb 21)
Regular Ticket @90 BYN
+
Combo Conference+Workshop Days (Feb 21+22)
Regular Ticket @270 BYN
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