ci/cd

Recursos de programación de ci/cd
Engineer at Dataworkz A passionate engineer at Dataworkz, Pieter gets a buzz from anything that's related to Engineering. It doesn't matter if it's a piece of code, a nicely setup K8s environment or a CI/CD pipeline - as long it is related to data or data streaming he's happy. When he's not working on Engineering, you're most likely to find him out on the water somewhere :-)
Developer Advocate at Apache APISIX A Developer Advocate with 15+ years experience consulting for many different customers in a wide range of contexts, such as telecoms, banking, insurance, large retail and in the public sector. Usually working on Java/Java EE and Spring technologies, but with focused interests like Rich Internet Applications, Testing, CI/CD and DevOps, Nicolas also doubles as a trainer and triples as a book author.
QA Manager at Plytix Fran Guerrero is an Agile ISTQB-certified specialist with more than 10 years of experience in the Quality Assurance field. He implements testing processes, QA strategies, innovative tools, and builds strong relationships across all teams. At Plytix, his team has become an essential part of the software quality process. From time to time, he enjoys speaking at QA-related forums on topics that include Agile Testing, Test Automation, and DevOps CI/CD methodologies.
Embedding quality through every phase of the software delivery lifecycle is not easy, but reducing risk and improving application quality is mandatory in a technologically competitive world. How can we improve our CI/CD pipelines in order to achieve this goal? Fran will walk you through different examples of scaling tests early, automation generated directly from requirements, enabling any team to learn from fast and continuous feedback as well as decrease technical debt, and, finally, improve business outcomes by making data-driven decisions about release readiness.
Hay muchas GitHub Actions muy poderosas que son muy poco conocidas. Vamos a repasar las que usamos en nuestros proyectos y preguntar al público (tendremos público presencial 👼) cuáles usan. 🔗 Enlaces vistos en el directo: 🏷️ PR size labeler: https://github.com/CodelyTV/pr-size-labeler 🐙 Listado Actions Codely: https://github.com/CodelyTV/?q=actions 🖼️ Optimización imágenes: https://github.com/calibreapp/image-actions ⚡ Curso Integración Continua con GitHub Actions: https://pro.codely.tv/library/integracion-continua-con-github-actions-51237/109857/about/ 🌈 Curso Automatización con GitHub Actions: https://pro.codely.tv/library/automatiza-tu-flujo-de-trabajo-con-github-actions-52283/113898/about/ 🚀 Curso Web Performance: https://pro.codely.tv/library/web-performance-168675/364571/about/ {▶️} CodelyTV ├ 🎥 Suscríbete: https://youtube.com/c/CodelyTV?sub_confirmation=1 ├ 🐦 Twitter CodelyTV: https://twitter.com/CodelyTV ├ 🧔🏻 Twitter Javi: https://twitter.com/JavierCane ├ 💂🏼 Twitter Rafa: https://twitter.com/rafaoe ├ 📸 Instagram: https://instagram.com/CodelyTV ├ ℹ️ LinkedIn: https://linkedin.com/company/codelytv ├ 🟦 Facebook: https://facebook.com/CodelyTV └ 📕 Catálogo cursos: https://bit.ly/cursos-codely
▬▬▬▬▬▬ Título de la Sesión ▬▬▬▬▬▬ Mejora del rendimiento del componente TreeFilter en el panel de contenidos ▬▬▬▬▬▬ Ponente ▬▬▬▬▬▬ Beltrán Rengifo Beltrán es Senior Frontend Engineer en Liferay y entre sus pasiones Javascript está en primer lugar. Vue, React, Nuxt, Next, SASS/LESS/BEM, Styled Components. JS del lado del servidor con Node y a veces Python o PHP. Intentando con ahínco asumir los conocimientos necesarios de Frontend devops como Docker, CI/CD con Gitlab/GitHub, Jenkins y Rancher. Intentando controlar Webpack desde 2017. Admirando las plataformas Vercel y Netlify. * Linkedin - https://www.linkedin.com/in/beltranrengifo/ * Twitter - https://twitter.com/BeltranRengifo ▬▬▬▬▬▬ Resumen ▬▬▬▬▬▬ En este caso veremos cómo el equipo de Liferay se planteó la necesidad de auditar el rendimiento del componente React TreeFilter y nos compartirán los resultados obtenidos. En la sesión se aprenderá a detectar, analizar y resolver problemas de rendimiento en un componente React JS, para ello se realizará: * Detectar mediante la realización de pruebas pesadas * Analizar concluyendo sobre las métricas * Resolver implementando las tareas más eficientes y asequibles En el camino mostraremos algunas implementaciones de JS dentro de los componentes de React, cómo probamos el rendimiento utilizando el componente OOTB Profiler de React, y las correcciones finales que implementamos en la rama master. Primero se presentará el componente, sus principales funciones y los inconvenientes que detectamos en el camino, para después mostrar el test de rendimiento, los resultados y la implementación de las mejoras. La agenda de la sesión de este miércoles será la siguiente: 18:30 - Bienvenida y Anuncios 18:40 - Meetup Enero 2022 - Mejora del rendimiento del componente TreeFilter en el panel de contenidos - Beltrán Rengifo. 19:25 - Q & A 19:30 - Networking (Mozilla Hubs) ▬▬▬▬▬▬ Organizadores de la sesión ▬▬▬▬▬▬ - - - ▬▬▬▬▬▬ LUGSpain ▬▬▬▬▬▬ Twitter - https://twitter.com/LUGSpain Slack - https://liferay.dev/chat Meetup - https://www.meetup.com/es-ES/Liferay-Spain-User-Group/
It is well known that data quality and quantity are crucial for building Machine Learning models, especially when dealing with Deep Learning and Neural Networks. But besides the data required to build the model itself, there is another often overlooked type of data required to build a production-grade Machine Learning Platform: Metadata. Modern Machine Learning platforms contain a number of different components: Distributed Training, Jupyter Notebooks, CI/CD, Hyperparameter Optimization, Feature stores, and many more. Most of these components have associated metadata including versioned datasets, versioned Jupyter Notebooks, training parameters, test/training accuracy of a trained model, versioned features, and statistics from model serving. For the dataops team managing such production platforms, it is critical to have a common view across all this metadata, as we have to ask questions such as: Which Jupyter Notebook has been used to build Model XYZ currently running in production? If there is new data for a given dataset, which models (currently serving in production) have to be updated? In this talk, we look at existing implementations, in particular, MLMD as part of the TensorFlow ecosystem.
Actualmente son muchas las organizaciones que buscan la Entrega de Valor Continuo. Acelerar, Garantizar y Maximizar el retorno de inversión de los Activos Digitales en producción empieza por sacar una fotografía del estado actual sobre el que comenzar a construir talento, conocimiento y buenas prácticas. ------------- Todos los vídeos del Festival Agile Trends en: https://lk.autentia.com/3d5nL7d ¡Conoce Autentia! - Twitter: https://goo.gl/MU5pUQ - Instagram: https://lk.autentia.com/instagram - LinkedIn: https://goo.gl/2On7Fj/ - Facebook: https://goo.gl/o8HrWX
Meetup #AperiTech della Community di PyRoma Speaker: Raffaele Colace Titolo: Come creare un progetto Django e React con pipeline CI/CD e con Kubernates. Abstract: Illustro il nostro template di sviluppo open-source per lo sviluppo, il test ed il delivery di progetti principalmente basati su Python, Django, PostgreSQL, uWSGI, React, Docker e Kubernetes che usiamo in produzione di progetti di medie e grandi dimensioni, per fornire servizi web o mobile. In questo talk mettiamo in pratica le regole dello sviluppo agile in ambito web usando il linguaggio ed il framework web che preferiamo con l'utilizzo di tecnologie di orchestrazione e CI/CD il tutto racchiuso nel nostro template open-source e riutilizzabile da tutti così com'è o adattabile anche con altri linguaggi di programmazione o framework. In definitiva vorremo portare l'esempio della nostra azienda dove, pur non avendo una figura dedicata esclusivamente ai processi specifici del DevOps ne usiamo l'approccio coordinandoci tramite il nostro template. Per restare aggiornato su tutti gli #AperiTech: Telegram #AperiTech https://t.me/aperitech Calendario del Developer https://bit.ly/devcalendar Codemotion Tech Community https://bit.ly/CodemotionTC
Meetup #AperiTech della Community di Torino .NET Azure DevOps e' una piattaforma che permette di gestire repository condivisi, pipeline di CI/CD e molto altro. In questa sessione abbiamo visto una panoramica di Azure DevOps con un focus particolare sulle pipeline di CI/CD. Speaker: Davide Bellone, uno sviluppatore backend con particolare interesse per il mondo Microsoft e .NET. Per restare aggiornato su tutti gli #AperiTech: Telegram #AperiTech https://t.me/aperitech Calendario del Developer https://bit.ly/devcalendar Codemotion Tech Community https://bit.ly/CodemotionTC