List of videos

EuroPython 2022 - Common Python Mistakes with Kubernetes, How They Can Cause Vulnerabilities and How to Solve Them! - presented by Flo Pachinger [Liffey Hall 2 on 2022-07-14] In this session, we will have a look at common mistakes in Python, that can cause serious code vulnerabilities, specifically for Kubernetes deployments of the code. We will subsequently have a look at what those vulnerabilities actually can result in and how your containerized application can get ""compromised"" as a result. We will also discuss how developer and security teams struggle to talk in a common language to prevent and mitigate these vulnerabilities. Lastly, we will see how you can prevent and mitigate these vulnerabilities in real-life using tools like Falco, TUF, Open Policy Agent and Bandit. We will also see how a CI/CD pipeline should look like, to build, test and deploy something in real-life. During this session you will learn a ton, see cool demos and all of the samples will be available to the attendees afterwards. My session will benefit the ecosystem by pointing out common mistakes that can be made when writing Python code and deploying this via Kubernetes. This can cause serious breaches when exploited by attackers. The goal of the session is to both educate attendees on these vulnerabilities, as well as on how to fix them. I will be talking about multiple open source projects that can secure code and deployment. I will not cover any commercial products. Falco TUF Open Policy Agent Bandit (not CNCF) GitLab (not CNCF) This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License http://creativecommons.org/licenses/by-nc-sa/4.0/

EuroPython 2022 - Creating great user interfaces on Jupyter Notebooks with ipywidgets - presented by Deborah Mesquita [Liffey Hall 2 on 2022-07-14] A useful Jupyter notebook that takes input from the user to generate results is a great candidate to become a web application, but usually data scientists don't have the front-end skills required to build one and deploy them. Using notebooks with ipywidgets can be a great solution to build teams' internal tools because we get the user-friendly widgets and don't need to worry about the deployment since it's all in Jupyter. This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License http://creativecommons.org/licenses/by-nc-sa/4.0/

EuroPython 2022 - Real-time browser-ready computer vision apps with Streamlit - presented by Yuichiro Tachibana [Liffey Hall 2 on 2022-07-14] I am the author of `streamlit-webrtc` and a member of the Streamlit Creators program (https://streamlit.io/creators) (selected community members). The repository of `streamlit-webrtc` is here: https://github.com/whitphx/streamlit-webrtc My lightning talk about `streamlit-webrtc` at PyCon JP 2021 is available: https://youtu.be/_LuLs8H1gJc Articles about this library: * Developing Web-Based Real-Time Video/Audio Processing Apps Quickly with Streamlit: https://towardsdatascience.com/developing-web-based-real-time-video-audio-processing-apps-quickly-with-streamlit-7c7bcd0bc5a8 * Real-Time Video Streams With Streamlit-WebRTC: https://betterprogramming.pub/real-time-video-streams-with-streamlit-webrtc-bd38d15f2ef3 As linked from the repo, demo apps I have developed are available online: * Demo showcase including real-time object detection: https://share.streamlit.io/whitphx/streamlit-webrtc-example/main/app.py * Source code: https://github.com/whitphx/streamlit-webrtc-example/blob/main/app.py * Real-time Speech-to-Text: https://share.streamlit.io/whitphx/streamlit-stt-app/main/app_deepspeech.py * Source code: https://github.com/whitphx/streamlit-stt-app * Real-time style transfer: https://share.streamlit.io/whitphx/style-transfer-web-app/main/app.py * Source code: https://share.streamlit.io/whitphx/style-transfer-web-app/main/app.py * Real-time Tokyo 2020 Pictogram: https://share.streamlit.io/whitphx/tokyo2020-pictogram-using-mediapipe/streamlit-app * Source code: https://github.com/whitphx/Tokyo2020-Pictogram-using-MediaPipe * Video chat: online demo is not available because it does not have an auth mechanism and is only for private use. * Source code: https://github.com/whitphx/streamlit-video-chat-example This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License http://creativecommons.org/licenses/by-nc-sa/4.0/

EuroPython 2022 - Use animated charts to present & share your findings with ipyvizzu - presented by Peter Vidos [Liffey Hall 2 on 2022-07-14] In this talk we'll cover the following topics: - The problem with the well-known chart taxonomies: starting from "what would you like to show" - Creating a generic chart morphing engine - Advantages of using animation for storytelling - from the presenter's and the audience's perspective - Examples and best practices of using ipyvizzu This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License http://creativecommons.org/licenses/by-nc-sa/4.0/

EuroPython 2022 - Scaling scikit-learn: introducing new sets of computational routines - presented by Julien Jerphanion [Liffey Hall 2 on 2022-07-14] scikit-learn is an open-source scientific library for machine learning in Python. Since its first release in 2010, the library gained a lot of traction in education, research and the wider society, and has set several standards for API designs in ML software. Nowadays scikit-learn is of one the most used scientific library in the world for data analysis. It provides reference implementations of many methods and algorithms to a userbase of millions. With the renewed interest in machine-learning based methods in the last years, other libraries providing efficient and highly optimised methods (such as for instance LightGBM and XGBoost for Gradient-Boosting-based methods) have emerged. Those libraries have encountered a similar success, and have put performance and computational efficiency as top priorities. In this talk, we will present the recent work carried over by the scikit-learn core-developers team to improve its native performance. This talk will cover elements of the PyData ecosystem and the CPython interpreter with an emphasis on their impact on performances. Computationally expensive patterns will then be covered before presenting the technical choices associated with the new routines implementations, keeping the project requirements in mind. At the end, we will take a quick look at the future work and collaborations on hardware-specialised computational routines. This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License http://creativecommons.org/licenses/by-nc-sa/4.0/

EuroPython 2022 - Simple data validation and setting management with Pydantic - presented by Teddy Crepineau [Liffey Hall 1 on 2022-07-14] When processing data, validating its structure and its type is critical. Bad record types or changes in structure can often result in processing errors or worst in wrong data output. Yet, solving this problem cleanly and efficiently can be challenging. It often results in complicated code logic and increases complexity; consequently decreasing code readability. Pydantic is an efficient and elegant answer to these challenges We expect you'll leave this talk with a good understanding of: - Existing challenges in data validation - What Pydantic Models, Validators, and Convertors are - How to leverage Pydantic in your day to day (using real-life examples) - [Bonnus] How to use Code Generation to create Pydantic Models from any data sources This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License http://creativecommons.org/licenses/by-nc-sa/4.0/

EuroPython 2022 - Managing complex data science experiment configurations with Hydra - presented by Michal Karzynski [Liffey Hall 1 on 2022-07-14] Data science experiments have a lot of moving parts. Datasets, models, hyperparameters all have multiple knobs and dials. This means that keeping track of the exact parameter values can be tedious or error prone. Thankfully you're not the only ones facing this problem and solutions are becoming available. One of them is Hydra from Meta AI Research. Hydra is an open-source application framework, which helps you handle complex configurations in an easy and elegant way. Experiments written with Hydra are traceable and reproducible with minimal boilerplate code. In my talk I will go over the main features of Hydra and the OmegaConf configuration system it is based on. I will show examples of elegant code written with Hydra and talk about ways to integrate it with other open-source tools such as MLFlow. This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License http://creativecommons.org/licenses/by-nc-sa/4.0/

EuroPython 2022 - Open Science: Building Models LIke We Build Open-Source Software - presented by Steven Kolawole [Liffey Hall 1 on 2022-07-14] Here, I elaborate on why we should develop tools that will allow us to build pre-trained models in the same way that we build open-source software. Specifically, models should be developed by a large community of stakeholders who continually update and improve them. Realizing this goal will require porting many ideas from open-source software development to building and training models, which motivates many threads of interesting research and opens up machine learning research for much larger participation. This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License http://creativecommons.org/licenses/by-nc-sa/4.0/

EuroPython 2022 - Synergize AI and Domain Expertise - Explainability Check with Python - presented by Pranjal Biyani [Liffey Hall 1 on 2022-07-14] We will go through the Why? How? and What? of Model Explainability to build consistent, robust and trustworthy models. We explore the inability of complex models to deliver meaningful insights, cause-effect relationships and inter-connected effects within data and how explainers can empower decision makers with more than just predictions. We evaluate an intuitive game-theory based algorithm, SHAP, with a working implementation in Python. We will also pin-point intersections necessary with domain experts with 2 practical industry applications to facilitate further exploration. This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License http://creativecommons.org/licenses/by-nc-sa/4.0/