List of videos

Traveling through a secure API in Python [EuroPython 2021 - Talk - 2021-07-30 - Ni] [Online] By Jessica Temporal I like to remember the places I have been and I always liked the idea of having a map of those places, so as any developer would do, I built a web application for that. In this talk, we will see how you can use Python and Auth0 together to build your very own "Where Have I Been" map! I will walk you through all the steps we will need starting from scratch. From building the first API endpoints, protecting the endpoints that create new markers, all the data manipulation, and even deployment! License: This video is licensed under the CC BY-NC-SA 4.0 license: https://creativecommons.org/licenses/by-nc-sa/4.0/ Please see our speaker release agreement for details: https://ep2021.europython.eu/events/speaker-release-agreement/

A Tale Of Python C Extensions And Cross-Platform Wheels [EuroPython 2021 - Talk - 2021-07-30 - Optiver] [Online] By Vinayak Mehta Have you ever wondered why Python is referred to as CPython and what is the Python C-API everyone keeps talking about? Come to this talk and find out! We'll briefly take a look at the C-API, how we can use it to extend Python's functionality, and how we can write Python C extensions using pybind11. After writing our Python C extension, we'll package it so users on all major operating systems can easily install it using pip. We'll take a look at how we can do this by building cross-platform wheels for Linux, macOS, and Windows and automatically pushing them to PyPI! License: This video is licensed under the CC BY-NC-SA 4.0 license: https://creativecommons.org/licenses/by-nc-sa/4.0/ Please see our speaker release agreement for details: https://ep2021.europython.eu/events/speaker-release-agreement/

Personal growth and the Python community [EuroPython 2021 - Talk - 2021-07-30 - Brian] [Online] By Yashasvi Misra I owe both my personal and professional growth to communities. As an engineering student in computer science, the first open tech community I joined was PyLadies. I have since then been actively involved in the Python community, always trying to learn and grow. Little did I expect how beneficial that would be. In this session, I will share my journey with the Python community, the impact it had on me, and how it boosted my self-confidence. If you don’t know where to start, join me to explore what I have learned so far. License: This video is licensed under the CC BY-NC-SA 4.0 license: https://creativecommons.org/licenses/by-nc-sa/4.0/ Please see our speaker release agreement for details: https://ep2021.europython.eu/events/speaker-release-agreement/

Simple, open, music recommendations [EuroPython 2021 - Talk - 2021-07-30 - Ni] [Online] By Sam Thursfield Can simple open source tools compete with the music recommendations provided by Spotify and other big names? This talk will look at how the open source world can stay relevant in a world where music listening has become dependent on commercial streaming services and users expect an element of recommendations. Expect to see small-tech solutions for music recommendations based around GNOME's Tracker search engine and the open, community-powered database Musicbrainz. License: This video is licensed under the CC BY-NC-SA 4.0 license: https://creativecommons.org/licenses/by-nc-sa/4.0/ Please see our speaker release agreement for details: https://ep2021.europython.eu/events/speaker-release-agreement/

Streamlit: The Fastest Way to build Data Apps [EuroPython 2021 - Talk - 2021-07-30 - Parrot [Data Science]] [Online] By Steven Kolawole When we think about building Python-based data science apps, we think of Flask. But there is a better option now. Streamlit. Streamlit is an open-source web framework that lets you create apps for your machine learning projects with deceptively simple Python scripts, in hours. It supports hot-reloading, so your app updates live as you edit and save your file. No need to mess with HTTP requests, HTML, JavaScript, etc. In a short sentence, there is no need to write any front-end code. All you need is your favorite editor and a browser. In this talk, we’ll go through how to build a very simple Streamlit app step-by-step. I will also review the pros and cons of Streamlit, as regards other popular Python web frameworks being used by Data Scientists and ML Engineers. License: This video is licensed under the CC BY-NC-SA 4.0 license: https://creativecommons.org/licenses/by-nc-sa/4.0/ Please see our speaker release agreement for details: https://ep2021.europython.eu/events/speaker-release-agreement/

Develop and deploy a Machine Learning pipeline in 30 minutes with Ploomber [EuroPython 2021 - Talk - 2021-07-30 - Ni] [Online] By Eduardo Blancas * Description * Development tools such as Jupyter are prevalent among data scientists because they provide an environment to explore data visually and interactively. However, when deploying a project, we must ensure the analysis can run reliably in a production environment like Airflow or Argo; this causes data scientists to move code back and forth between their notebooks and these production tools. Furthermore, data scientists have to spend time learning an unfamiliar framework and writing pipeline code, which severely delays the deployment process. Ploomber solves this problem by providing: A workflow orchestrator that automatically infers task execution order using static analysis. A sensible layout to bootstrap projects. A development environment integrated with Jupyter. Capabilities to export to production systems (Kubernetes, Airflow, and AWS Batch) without code changes. * Who and why * This talk is for data scientists (with experience developing Machine Learning projects) looking to enhance their workflow. Experience with production tools such as Airflow or Argo is not necessary. The talk has two objectives: Advocate for more development-friendly tools that let data scientists focus on analyzing data and take off the overhead of popular production tools. Demonstrate an example workflow using Ploomber where a pipeline is developed interactively (using Jupyter) and deployed without code changes. * Resources * Show your support with a ⭐️ on GitHub! https://github.com/ploomber/ploomber Join our community! http://community.ploomber.io/ License: This video is licensed under the CC BY-NC-SA 4.0 license: https://creativecommons.org/licenses/by-nc-sa/4.0/ Please see our speaker release agreement for details: https://ep2021.europython.eu/events/speaker-release-agreement/

Learn CPython by breaking it [EuroPython 2021 - Talk - 2021-07-30 - Optiver] [Online] By Cristián Maureira-Fredes One of the main reasons that stop people to write Python C extensions and even attempt to contribute to CPython is due to the fact that you need to know another programming language, C. Wait, don't go! don't be scared by the fact that you will hear about C, this talks objective is not that! but only to learn the essential bits to get more comfortable around CPython. By understanding the internals, you will feel like the same as that time in your childhood when you took something apart, then re-assemble it again and it worked (if that was not the case, this will be your first time I promise). During this talk, you will go on a journey to understand how Python works, and which are the places you can look to understand the implementation of different Python details, from the whole interpretation process, until look what's lies under the module/types functions you use everyday. You will get the power to add new functionality to Python and even how to create your first C-based extension module. If you are not familiar with C, don't worry, I will guide you every time some C code is shown, and if you know some C, then you will be able to sit back and relax during this ride. License: This video is licensed under the CC BY-NC-SA 4.0 license: https://creativecommons.org/licenses/by-nc-sa/4.0/ Please see our speaker release agreement for details: https://ep2021.europython.eu/events/speaker-release-agreement/

Learn Python automation by recreating Git Commit from scratch [EuroPython 2021 - Talk - 2021-07-30 - Brian] [Online] By Matteo Bertucci This talk is divided in two parts: the first one will explore how the Git key-value storage work and the second part will walk through recreating the fundamental git commit command with less than 80 lines of Python. You can follow this talk at home using shell commands provided by the speaker and explore Git on your own. The code is also a great way to familiarize yourself with making and structuring Python utility scripts. It is also a great way to present more intermediate programming concept such as recursion. All the commands and code are wrote to be as easy to understand as possible and commented by the speaker, as this talk aims to be simple to follow by Python and Git beginners. Good programming practice will be quickly shown, such as the use of f-strings and the usage of pathlib over os.path. The final application is kept pretty barebone for brevity sakes, although challenges with some hints are given at the end of the talk as an exercise. The final goal of this talk is to give the watcher a feel on how they can use Python in their every day life to automate different tasks, and some pointers toward good programming practices and continue in their Python journey. This talk is inspired from an unpublished article wrote by myself that you can consult here: https://www.notion.so/Recreating-Git-Commit-in-Python-269f72bbc69d4f7e8aa64f67cc7d95d1 The final source code can be found here: https://gist.github.com/Akarys42/47085d16978947039279d75e1773725e License: This video is licensed under the CC BY-NC-SA 4.0 license: https://creativecommons.org/licenses/by-nc-sa/4.0/ Please see our speaker release agreement for details: https://ep2021.europython.eu/events/speaker-release-agreement/

Production ML Monitoring: Outliers, Drift, Explainers & Statistical Performance [EuroPython 2021 - Talk - 2021-07-30 - Parrot [Data Science]] [Online] By Alejandro Saucedo Session Description The lifecycle of a machine learning model only begins once it's in production. In this talk we provide a practical deep dive on best practices, principles, patterns and techniques around production monitoring of machine learning models. We will cover standard microservice monitoring techniques applied into deployed machine learning models, as well as more advanced paradigms to monitor machine learning models with Python leveraging advanced monitoring concepts such as concept drift, outlier detector and explainability. We'll dive into a hands on example, where we will train an image classification machine learning model from scratch using Tensorflow, deploy it, and introduce advanced monitoring components as architectural patterns with hands on examples. These monitoring techniques will include AI Explainers, Outlier Detectors, Concept Drift detectors and Adversarial Detectors. We will also be understanding high level architectural patterns that abstract these complex and advanced monitoring techniques into infrastructural components that will enable for scale, introducing the standardised interfaces required for us to enable monitoring across hundreds or thousands of heterogeneous machine learning models. Benefits to ecosystem This talk will benefit the ecosystem by providing cross-functional knowledge, bringing together best practices from data scientists, software engineers and DevOps to tackle the challenge of machine learning monitoring at scale. During this talk we will shed light into best practices in the python ecosystem that can be adopted towards production machine learning, and we will provide a conceptual and practical hands on deep dive which will allow the community to both, tackle this issues and help further the discussion. License: This video is licensed under the CC BY-NC-SA 4.0 license: https://creativecommons.org/licenses/by-nc-sa/4.0/ Please see our speaker release agreement for details: https://ep2021.europython.eu/events/speaker-release-agreement/