List of videos

Full title: Talks - Improve Your ML Projects: Embrace Reproducibility and Production Readiness with Kedro Presented by: Juliana Ferreira Alves The more complex your ML project becomes, the more challenging it is to manage and deploy it into production. Beyond reproducibility, factors such as flexibility, readability, and production readiness play crucial roles in enhancing project efficiency. This is where Kedro comes in, a framework specifically designed to take your ML projects to another level. In this talk, I will introduce Kedro and explain the contexts in which it should be used. You'll gain hands-on experience with Python, learning how to smoothly integrate Kedro into your projects. This will enable you to spend less time on tedious 'plumbing' work and more time focusing on solving new problems.

Qiskit is a python-based open-source toolkit for working with quantum computers. In this talk, we describe our work of developing a hybrid Quantum-Classical Machine Learning (ML) framework using Qiskit. This talk will cover how to build quantum circuits and classical models for ML tasks such as text classification and sentiment analysis. Slides: https://pycon-assets.s3.amazonaws.com/2024/media/presentation_slides/125/2024-05-18T18%3A10%3A14.519771/Mahmud_Pycon_2024.pdf

Ruff is an extremely fast Python linter and code formatter, written in Rust. With Ruff, projects can replace dozens of static analysis tools with a single dependency, all while executing 10x, 100x, or even 1000x faster. Over the past year, Ruff has grown to millions of downloads per month, and now powers static analysis for the largest projects in the Python ecosystem, including NumPy, Pandas, PyTorch, LangChain, and more. This talk will open with a brief overview of Ruff’s functionality before diving into its internals, with a focus on performance. In particular, we’ll look to answer the question: what does it take to build a developer tool that’s orders of magnitude faster than the alternatives? Our exploration will be grounded in specific optimizations and design decisions that make Ruff fast. Along the way, we’ll also explore the broader tradeoffs that come with building developer tools for Python, in Rust. Although Ruff is written in Rust, no Rust knowledge is required. Instead, this talk is aimed at those interested in building performant developer tools for any domain, in any language. Slides: https://pycon-assets.s3.amazonaws.com/2024/media/presentation_slides/22/2024-05-16T16%3A51%3A47.243275/PyCon.pdf

Is your relationship with maths... complicated? Do you hate doing simple, repetitive, error prone calculations that you know your computer could trivially do? Have you ever been disappointed by Wolfram Alpha's inability to calculate your problems with working out? Then book this session, and I'll show you the basics of SymPy, and cover some university level math solutions by demonstrating a calculator to solve and print working out for them. I’ll also present how Jupyter can be used to display and submit parts of your assignment, and finally how you can help contribute to the computational maths community. Slides: https://pycon-assets.s3.amazonaws.com/2024/media/presentation_slides/17/2024-05-13T13%3A34%3A18.498350/slides.txt

Full title: Acknowledging Women’s Contributions in the Python Community Through Podcast Presented by: Cheuk Ting Ho Georgi Ker Mariatta Tereza Iofciu The Python community has been making efforts in improving the diversity and representation among its members. There are examples of success stories such as PyCon US Charlas, PyLadies, Djangonaut, and Django Girls. Yet in the Python podcast community, women are still underrepresented, making up only 17% of invited guests among the popular podcast series. Being a guest in a podcast is a privilege, and an opportunity to influence the Python community. There are many women and underrepresented group members who have made impactful contributions to the Python community globally, and they deserve the recognition and to be heard by the rest of us. Disheartened by the lack of representation by women on Python podcasts, and inspired by others who have shown us how diversity in the community can be improved through intentionality, we decided to start a podcast with a goal to highlight their voices so that they could receive the recognition they deserve. In this talk,earn about them, and about our podcast series. We’ll also share how you can further help out cause in improving representation and diversity in the Python community. Goal To raise awareness of the underrepresentation of certain groups, especially women. To acknowledge the progress made by the Python community and what can be done further to continue the improvement. Target Audience Anyone who cares about the diversity and inclusion progression in the Python community. Community leaders who want to be allies. Slides: https://pycon-assets.s3.amazonaws.com/2024/media/presentation_slides/94/2024-05-16T15%3A13%3A56.621886/Acknowledging_Womens_Contributions_in__UkhnXs1.pdf

This is a talk about how to develop a game in Python such that you'll be able to take a cool idea all the way to a Steam release. Nearly every talk about game dev tools will introduce the tool (such as pygame) and conclude with a minimal example ("press the arrow keys to move the square"). This talk isn’t about moving a square; it’s about everything else. More specifically, it's a talk about everything else in Python. I've made three Python games and several others in other languages--in this talk, I will explain why Python is often the best tool for gamemaking. I made a procedurally-generated monster collecting game in Python that I released on itch.io and Steam. This talk is about how I made decisions over the course of nine months to ensure that development was fast, testing was robust, graphical effects were weird, and gameplay was fun. Slides: https://pycon-assets.s3.amazonaws.com/2024/media/presentation_slides/76/2024-05-12T17%3A38%3A02.264759/Procedurally_Generated_Monsters.pdf

I once heard the question of whether there was a real need to create more support subgroups for minorities in Python: whether, at the end of the day, the community would be more segregated than united. When I passed this on to a few people, a new demand emerged: that there were difficulties in creating more diverse teams. Based on the organization of meetups, regional events and ethnographic research among different tech communities, this talk seeks not only to present possible solutions for increasing diversity in python environments (such as academia, companies and communities), but also to instigate debate about where we can improve and how to truly listen to pythonistas. This talk also aims to explore already established tools - such as the Code of Conduct - and evolve them in the light of new minorities and to provide a safe space for learning and connections. Slides: https://pycon-assets.s3.amazonaws.com/2024/media/presentation_slides/104/2024-05-15T02%3A25%3A55.927144/PyCon_US_2024_Final_Version.pdf

As we are in an era of big data where large groups of information are assimilated and analyzed, for insights into human behavior, data privacy has become a hot topic. Since there is a lot of private information which once leaked can be misused, all data cannot be released for research. So, how should privacy be protected in the environment where data is stored and shared at such an escalating pace? One might think that simply making personally identifiable fields in the dataset anonymous might be useful, but this can lead to the entire dataset becoming useless and not fit for analysis. And research has proven that by statistically studying both the datasets, private information can easily be re-extracted! The session will start with a brief on the current standards of privacy, and the possible risks of handling customer data. This will lay the foundation for introducing Differential Privacy, a cutting-edge technique of cybersecurity that claims to preserve an individual’s privacy, by manipulating data in such a way as to not render it useless for data analysis. Attendees will gain an insight into the concept of Differential Privacy, how it is employed to minimize the risks associated with private data, its practical applications in various domains, and how Python eases the task of employing it in our models with PyDP. As the talk progresses, a walkthrough of a real-life practical example, along with a nifty visualization will acquaint the audience with PyDP, and how differential private results come out to be in approximation to what unfiltered data would have provided. Slides: https://pycon-assets.s3.amazonaws.com/2024/media/presentation_slides/108/2024-05-17T16%3A05%3A59.414232/Differential_Privacy.pptx.pdf

Python is one of the programming languages that has a huge open-source supply chain. There are over 400,000 Python packages on Python Package Index (PyPI) and many more on other registries like conda-forge, mostly for scientific libraries. Making sure this and the wider Python ecosystem are secure is a huge job and requires consistent contributions. Thanks to OpenSSF’s Alpha-Omega project and AWS, we now have a PSF Security Developer-in-Residence and PyPI Safety & Security Engineer whose responsibility includes a security audit of the PyPI codebase and infrastructure, improving security practices, and establishing metrics on security posture to show the impact. In this talk, we will go over the work that has been done by the PSF security team and what the best practices for Python library maintainers and users are. Goal The goal of this talk is to draw awareness of security, especially in Python's ecosystem. It highlights how PSF is helping the community, on the other hand, it also provides advice for a user or community member on what can be done to make sure they are using Python safely. Target audiences Ths talk is for anyone in the Python community. If you are using Python, or your company is using Python. This talk is for you. Slides: https://pycon-assets.s3.amazonaws.com/2024/media/presentation_slides/83/2024-05-17T13%3A54%3A25.653486/python-safer.pdf