Joel Grus is a principal engineer at Capital Group, where he leads a team focused on machine learning and search relevance. Previously he worked as a research engineer at the Allen Institute for AI, a software engineer at Google, and a data scientist at a variety of startups.

He is the author of Data Science from Scratch and Ten Essays on Fizz Buzz, but he may be best known for not liking Jupyter notebooks. He lives in Seattle.

Anthony Shaw is a Python advocate from Sydney, Australia. Anthony is a contributor to many open-source communities. Running and contributing to a number of popular open-source tools for DevOps, Security, Automation and Code Quality.

Anthony has been recognized for his contribution to open-source, including a Fellow of the Python Software Foundation and a member of the Apache Software Foundation.

Anthony runs a Python blog and YouTube channel and has recently published a book on the Python compiler.

I'm tiangolo (Sebastián Ramírez), the creator of FastAPI, Typer, and other open-source tools.

I'm currently a developer at Explosion in Berlin, Germany.

If you have used CLI (Command Line Interface) applications, you are probably familiar with shell completion (TAB completion) and how much it helps.

In this talk, you'll learn how to quickly create CLI apps that have automatic shell completion with Typer (FastAPI's little sibling), by declaring standard Python type annotations in your functions.

Typer will do the heavy lifting of handling shell completion for all the major shells, auto-documentation of your CLI, and data validation based on your type annotations.

You can start with just a simple script for yourself, in minutes. And also grow as big as you need, to create complex CLI apps with multiple levels of sub-command groups and many other features.

All by writing minimal code, based on standard type annotations, with autocompletion in your editor, and an intuitive design based on FastAPI.

Mariatta is a Python Core Developer, Staff Software Engineer at Uplight, and the Vancouver PyLadies co-organizer, and one of the founding members of the PyCascades conference.

She moved to Canada almost two decades ago, and now lives in Vancouver with her husband and two children. In her free time, she contributes to open source, builds GitHub bots, and fixes typos.

I consider myself relatively new to the open-source world; my first open-source contribution was in 2016. Since then, I've continued to actively contribute to open source and specifically to core Python and Python libraries.

Pretty soon I found myself being given commit rights to other people's open-source projects. It's been quite a journey. Being a new open-source contributor has its own challenges, and being a new open-source maintainer brings another set of unique challenges.

In this talk, I will share my journey and the things I've learned along the way, and some advice for other aspiring open source maintainers and contributors.

Matteo is a Senior Scientist in the Gray Systems Lab (GSL) at Microsoft, working on scalable Machine Learning systems. Before Microsoft, he was a Postdoctoral Scholar in the CS Department at the University of California, Los Angeles, working on Big Data systems.

Prior to joining UCLA, he was a researcher at the Qatar Computing Research Institute, and at the Institute for Human and Machine Cognition.
He obtained his PhD in Computer Science from the University of Modena and Reggio Emilia.

Karla is a Senior Research Software Development Engineer in the Gray Systems Lab (GSL) at Microsoft. She finished her PhD in Computer Science at the University of Maryland, College Park in 2015.

After graduating, Karla spent 3 years as a research scientist at Intel Labs and joined Microsoft in 2018.
Her research interests are broad, and generally enjoys scalability and performance challenges related to systems infrastructure.

Machine Learning (ML) adoption in the enterprise requires simpler and more efficient software infrastructure. Model scoring, the process of obtaining prediction from a trained model over new data, is a primary contributor to infrastructure complexity and cost, as models are trained once but used many times.

Hummingbird is a novel approach to model scoring, which compiles featurization operators and traditional ML models (e.g., decision trees) into a small set of tensor operations. This approach inherently reduces infrastructure complexity and directly leverages existing investments in Neural Networks' compilers and runtimes to generate efficient computations for both CPU and hardware accelerators.

Hummingbird performance are competitive and even outperforms hand-crafted kernels while enabling seamless end-to-end acceleration of ML pipelines.

Hummingbird is open source, part of the PyTorch Ecosystem, and someone even mentioned it as one of the top 10 Python libraries of 2020!

I have been working in Machine learning and Data science for several years in a few different IT companies. In particular, I enjoy exploring interesting world problems and solving them with state-of-the-art techniques.

I have developed several open-source python packages, I am the core-contributor of `pytorch-lightning` and actively participating in other well-known projects.

Gene is an artist and a programmer who is interested in autonomous systems, collective intelligence, generative art, and computer science. He is a collaborator within numerous open-source software projects, and gives workshops and lectures on topics at the intersection of code and art.

Gene initiated ml4a, a free book about machine learning for creative practice, and regularly publishes video lectures, writings, and tutorials to facilitate a greater public understanding of the subject.

This talk will explore the implications of hyper-realistic generative modeling to computer art. As deep learning-based generative models like StyleGAN2 and GPT-3 inflate to unprecedented sizes, they become capable of producing photorealistic imagery and Turing-test level text, opening up numerous and controversial applications.

We'll show how some of these techniques have been re-purposed for artistic exploration lately, as well as review some of their more creative possibilities.

Ian is a Chief Data Scientist and Coach, he's helped co-organise the annual PyDataLondon conference with 700+ attendees and the associated 11,000+ member monthly meetup.

He runs the established Mor Consulting Data Science consultancy in London, gives conference talks internationally often as keynote speaker and is the author of the bestselling O'Reilly book High Performance Python (2nd edition).

He has 18 years of experience as a senior data science leader, trainer and team coach. For fun he's walked by his high-energy Springer Spaniel, surfs the Cornish coast and drinks fine coffee. Past talks and articles can be found at: https://ianozsvald.com/

"My Pandas is slow!" - I hear that a lot. We'll look at ways of making Pandas calculate faster, help you express your problem to fit Pandas more efficiently and look at process changes that'll make you waste less time debugging your Pandas.

By attending this talk you'll get answers faster and more reliably with Pandas.

Vitaliy is from Kharkiv, Ukraine
He works in IT for more than 15 years. 12+ with Python
Vitaliy participated in various types of projects with high performance requirements.

Graduate of Belarusian State University (Faculty of Radiophysics and Computer Technologies).

Developer, Tech Lead, Researcher, Trainer at ISsoft and Coherent Solutions. Machine Learning group and Intelligence Solutions Department co-founder.

Aspect-based Sentiment Analysis is a challenging task of text analysis in e-Commerce. It researches and develops related ideas for text Sentiment Analysis to obtain Sentiment Analysis for each aspect in a text.

The proposed approach provides algorithms for aspects spotting based on semantic analysis of a text and estimating sentiment for the context of the aspects. Approach capabilities are perfectly matched for goods review analysis. In the presentation, it will be considered the solution based on BERT transformation and the development of a service for deploying the resulting model.

Python core developer, Python 3.8 and 3.9 release manager, creator of Black, pianist, dad.

Likes building synthesizers, immersive single-player role playing games like Fallout, and single malt Scotch whisky.

Programming languages with dynamic type systems as well as freeform document databases give us a big productivity boost early on in the project.

On the other hand, strong static type systems and relational databases protect us against an entire category of programming mistakes, and aid in code maintenance by helping IDEs auto-complete, jump to definition, refactor, and so on.

Is there a workable compromise? Can we somehow reap the benefits of both? Let's find out!

MinJae (a.k.a mingrammer) is the creator of Diagrams.
He has used Python for a long time to build automation and productivity tools.

Diagrams let you draw the cloud system architecture in Python code. It was born for prototyping a new system architecture design without any design tools.

In this talk, I will introduce the Diagrams as a whole and share why I made the Diagrams, and how it was designed and built.

Thomas is the CEO at PyMC Labs (www.pymc-labs.io). Prior to that Thomas was the VP of Data Science at Quantopian, where he used probabilistic programming and machine learning to help build the world's first crowdsourced hedge fund.

He is an author of the popular PyMC3 package — a probabilistic programming framework written in Python. He holds a PhD from Brown University.

Bayesian modeling is an extremely powerful tool in solving data science problems across different domains. And while user-friendly modeling packages like PyMC3 exist, understanding the underlying concepts still provides a challenge for many newcomers. The main reason is that usually, statistics is taught by statisticians who provide formulas with little regard for intuition.

In this talk I will take the opposite approach: throw all math out the window and explain the underlying concepts in an intuitive way.

Emeli is a Co-founder and CTO at Evidently AI, a startup developing tools to analyze and monitor the performance of machine learning models.
Earlier, she co-founded an industrial AI startup and served as the Chief Data Scientist at Yandex Data Factory. She led over 50 applied ML projects for various industries - from banking to manufacturing. Emeli is a data science lecturer at GSOM SpBU and Harbour.Space University.

She is a co-author of the Machine Learning and Data Analysis curriculum at Coursera with over 100,000 students.

She also co-founded Data Mining in Action, the largest open data science course in Russia.

ML-based services are different from traditional software applications. Once they are in production, we have to keep an eye not only on the code, but also on the data and model performance.

In this talk, I will share what new failure modes to prepare for, and how to set up model monitoring using open-source tools.

Some pictures
from the last offline conference

See 261 more on our Facebook page

How it felt over the last years

2019 Recap Video (115 seconds)
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2020 Recap Video (91 seconds)
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Programme committee

Nikolay Saskovets

Passionate Software Engineer

Nikita Grishko

Software Engineer

Dmitriy Karpov

Software Engineer

Stas Rudakou

Director of Engineering

Roman Imankulov

Python developer who loves web

Yuriy Guts

Principal Machine Learning Engineer

Andrii Soldatenko

Senior Software Engineer

General Partner

ISsoft is an international IT company founded in 2004 as a subsidiary of Coherent Solutions Inc, recognized by Software 500, Fast 50, Inc. 5000, Clutch Top-100, official partner with Microsoft, Xamarin and Amazon Web Services.

Company strives to create a work environment that is both productive and comfortable. What brought them to the top are ISsoft's people. They value every aspect of their contributions and strive to make ISsoft a place for career growth and opportunities.

ISsoft focuses on top-notch IT-solutions for North American and European markets. 100 of clients trust ISsoft to work on product software development, IT-consulting, data and analytics, machine learning, mobile app development, DevOps and cloud, Salesforce development and more.

Writing code for pleasure. 14 years in software engineering, 7 of them — with Python.

Organizer of the Minsk Python Meetup and sub-events around.

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.

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.

Roman writes Python code for more than a decade. Occasionally he shares his experience in blog posts and talks.

An active member of Python communities in Russia, Belarus, and Portugal.
Organized PyCon conferences in Russia and Belarus and various Python events in Porto.

Roman believes that programming is a skill that opens many doors and is eager to help people follow their passion for becoming developers.
https://roman.pt

A seasoned machine learning engineer with 15 years of industry experience in data science and software architecture.

His primary interests are productionalizing data science, AutoML, time series forecasting, and processing spoken and written language.

He teaches AI and ML at UCU, competes on Kaggle, and has led multiple international data science and engineering teams.

Yuriy currently works as Principal Machine Learning Engineer at DataRobot.