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Видео на тему: Профессиональное Программирование - видео

Before understanding Docker, it is very important to know what was happening before Docker? what is the server and application structure, etc. in this video I tried to explain the structure of the application in physical server.How you can build a fully functional Kubernetes cluster with a total budget of $300. DevNet Lightning Talk by Julio Gomez. Fast-paced, demo-driven presentations that will inspire you to learn more. DEVLIT-4017 Visit http://cs.co/cleur2020talks for more information. Did you ever feel the desire to build your own Kubernetes cluster from scratch? There is something visceral about creating, probably coming from your childhood Lego memories. The problem is that the older you get, the more expensive those building blocks are. Building a Kubernetes cluster would usually require multiple servers, switches, power, ventilation… not cheap. In this session, we will explore how you can build a fully functional Kubernetes cluster with a total budget of $300. You will learn everything you need: from the required shopping list, to assembling the different parts, to installing and configuring all required software, to deploying a real application inside and offer its service to the outside world. And the best part… the cluster will be the size of a banana. DevNet Lightning Talk by Julio Gomez. Fast-paced, demo-driven presentations that will inspire you to learn more. DEVLIT-4017 Visit http://cs.co/cleur2020talks for more information.Sharpen That Edge! How a Service Mesh Enhances EdgeComputeOps - Marino Wijay & Kevin Dorosh, Solo.io Sometimes you go all in on the cloud; sometimes you need to sharpen that Edge a bit. When pursuing Edge Computing, the largest considerations for adoption are: - Ease of deployment - Zero-trust security posture - Resource allocation and consumption - Telemetry and Observability - Latency and application response times - Resilience and reliability Large enterprises in heavily regulated industries or the public sector must adopt practices like a zero-trust security posture both inside and at the edge of its application networks. They must simultaneously be able to determine application performance through telemetry, and mitigate issues. They need to ensure the resilience & reliability of the edge in the face of catastrophe, like a cluster or region failure. What's the right approach to meeting these conditions? Enter Ambient Mesh, the perfect vehicle for meeting these challenges! This talk dives into how Ambient Mesh offers a revolutionary data-plane architecture for Edge Computing. Ambient Mesh can configure both perimeter and internal proxies to deploy an enhanced security posture while slashing operational complexity and enabling incremental mesh adoption, all while reducing cost and computational overhead at the Edge.Resources from the video: - HashiConf Digital June Sessions: https://hashiconf.com/digital-june/wa... - HashiCorp Cloud Platform: https://www.hashicorp.com/cloud-platform - Consul 1.8 Release Blog: https://www.hashicorp.com/blog/announ... - Sign up for HashiConf Digital October: https://hashiconf.com/digital-october/ - If you liked this video and want to see more from HashiCorp, subscribe to our channel: https://www.youtube.com/c/HashiCorp?sub_confirmation=1 To learn more, visit our hands-on interactive lab environment, HashiCorp Learn: https://learn.hashicorp.com/ HashiCorp is the leader in multi-cloud infrastructure automation software. The HashiCorp software suite enables organizations to adopt consistent workflows to provision, secure, connect, and run any infrastructure for any application. HashiCorp open source tools Vagrant, Packer, Terraform, Vault, Consul, Nomad, Boundary, and Waypoint are downloaded tens of millions of times each year and are broadly adopted by the Global 2000. Enterprise versions of these products enhance the open source tools with features that promote collaboration, operations, governance, and multi-data center functionality. For more information, visit: www.hashicorp.com or follow us on social media: Twitter: @hashicorp LinkedIn: https://www.linkedin.com/company/hashicorp Facebook: https://www.facebook.com/HashiCorpLearn more about GKE L4 ILB Subsetting → https://goo.gle/3ac5P7V Overview of GKE Multi-pod CIDRs → https://goo.gle/3FmooVx Scale DNS for GKE with Cloud DNS → https://goo.gle/3iFf3yb Welcome back to What’s New in Networking where we keep you up-to-date on Google Cloud networking. In this episode, Cynthia Thomas gives you the latest in Anthos and compute networking. Watch to learn about the GKE L4 ILB subsetting feature, multi-pod CIDRs, and more! Chapters: 0:00 - Intro 0:26 - GKE L4 ILB Subsetting 1:18 - GKE Multi-pod CIDRs 2:30 - Cloud DNS for GKE 3:15 - GKE Headless Services DNS records 3:41 - Bring your own IP 4:29 - Conclusion VPC-scope DNS for enabling headless GKE services across clusters → https://goo.gle/3oz2vwe Bring Your Own IP → https://goo.gle/3iDWF9b Watch more episodes of What’s New In Networking → http://goo.gle/WhatsNewInNetworking Subscribe to Google Cloud Tech → https://goo.gle/GoogleCloudTech #WhatsNewInNetworking product: Cloud - Networking - Cloud CDN; fullname: Stephanie Wong, Cynthia Thomas;Vagrant Docker Registry Server localData Works MD February 2021 - https://www.meetup.com/DataWorks/events/275232955/ ---------------------------------------- ML Design Patterns and Designing ML Infrastructure Design patterns are formalized best practices to solve common problems when designing a software system. As machine learning moves from being a research discipline to a software one, it is useful to catalog tried-and-proven methods to help engineers tackle frequently occurring problems that crop up during the ML process. In this talk, I will cover five patterns (Workflow Pipelines, Transform, Multimodal Input, Feature Store, Cascade) that are useful in the context of adding flexibility, resilience and reproducibility to ML in production. For data scientists and ML engineers, these patterns provide a way to apply hard-won knowledge from hundreds of ML experts to your own projects. Anyone designing infrastructure for machine learning will have to be able to provide easy ways for the data engineers, data scientists, and ML engineers to implement these, and other, design patterns. ---------------------------------------- Lak is the Director for Data Analytics and AI Solutions on Google Cloud. His team builds software solutions for business problems using Google Cloud's data analytics and machine learning products. He founded Google's Advanced Solutions Lab ML Immersion program and is the author of three O'Reilly books and several Coursera courses. Before Google, Lak was a Director of Data Science at Climate Corporation and a Research Scientist at NOAA. Lak can be reached on Twitter at @lak_gcp ---------------------------------------- Publications: Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps - https://www.amazon.com/Machine-Learning-Design-Patterns-Preparation/dp/1098115783Kubernetes Training - Web Terminal It can mitigate the user's typing and enjoy the course on the fly.En esta charla, nos presentan un demo de pruebas de integración con Docker en Azure, un ejemplo de como Simpat tech utiliza esta tecnologías en su día a día. #talks2code #bestplacetocode #devops #DockerRex sur la migration d'une société de Heroku (app engine GCP like), Github et CircleCI en Saas vers Kubernetes (via Helm et Vault), et gitlab (avec gitlab-ci) pour la CI/CD. https://parisjs.org/meetup/2019-03-27Los comentarios constructivos son siempre bienvenidos. Los tienes disponible aquí abajo. Hay más contenido en los siguientes enlaces: - Web http://www.inigoserrano.com - Twitter https://twitter.com/inigoserrano - Youtube https://www.youtube.com/user/inigoserrano1 - Inscribiendote en mi newsletter http://www.inigoserrano.com/ya-tenemos-newsletter/ Puedes formate con un certificado de finalización - Kubernetes: https://www.udemy.com/kubernetes-sencillo-para-desarrolladores/?couponCode=K8S_BLOG - OpenShift: https://www.udemy.com/openshift-sencillo-para-desarrolladores/?couponCode=OPENSHIFT_BLG - Docker: https://www.udemy.com/docker-sencillo-para-desarrolladores/?couponCode=DOCKERSENCILLO_BLG2 - Git con SourceTree: https://www.udemy.com/git-sencillo-para-desarrolladores-con-sourcetree/?couponCode=GITSENCILLO_NWS - Git con Eclipse: https://www.udemy.com/git-sencillo-para-desarrolladores-con-eclipse/?couponCode=EGITSENCILLO_NWS Puedes apoyar este contenido: - Dándole al like de arriba - Suscribiendote al canal - Paypal en: https://www.paypal.me/inigoserranoIf You Need Service For Linux, RocketChat Service, MongoDB, NOdeJS contact with me: Skype: zobaer.ahmed5 Whatsapp: +8801818 264577 Telegram: +8801818 264577 Email: mystudymytech@gmail.com Linkedin: https://www.linkedin.com/in/linuxintellect/ Twitter: https://twitter.com/linuxintellect1 Gist: https://gist.github.com/LinuxIntellect/8849b39674dd61db7cc6b6dee74b98df ============================================================================== What is Chat Service? A chat service is any online service or technology that enables text messages to be translated in real time between participants. Often used in customer service as “live chat,” a chat service connects a customer service agent with a customer for a real-time, instantaneous conversation. What is the difference between chat and message? As nouns the difference between message and chat is that message is a communication, or what is communicated; any concept or information conveyed while chat is (uncountable) informal conversation or chat can be (mining|local use) mining waste from lead and zinc mines. What is RocketChat? Rocket. Chat is a Web Chat Server, developed in JavaScript, using the Meteor fullstack framework. It is a great solution for communities and companies wanting to privately host their own chat service or for developers looking forward to build and evolve their own chat platforms. Who uses rocket chat? Today, Rocket. Chat is an open source team collaboration platform that enables banks, NGOs, startups, and governmental organizations to have their own chat tool, customize its look and feel, choose their users, and securely manage data. FEATURE 1. Rocket.Chat uses cutting edge machine learning for automatic real-time message translation between users. 2. Rocket.Chat - Free, Open Source, Enterprise Team Chat. #RocketChat #MongoDB #Linux #Nodejs #Yarn #NPM #NVM #GItTouch Portal и Macro Deck. Бесплатные альтернативы к Stream Deck и Loupedeck. Touch Portal и Macro Deck - две программы, которые превратят ваш смартфон или планшет в устройства управления программами на ПК. С помощью этих программ вы можете создавать "горячие кнопки" и макросы. Выводих их на экран Android устройств как иконки. Отлично подходят для стримеров. !Короче, посмотрите видео и будет всё понятно ;) Скачать можно здесь. Touch Portal: https://www.touch-portal.com/ Macro Deck: https://macrodeck.org/ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Буду очень благодарен за поддержку в виде чашечки ☕️: https://www.buymeacoffee.com/RomNero ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Предложениям пишите на: infotube@romnero.de ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~Houman Behzadi, President & Chief Product Officer at #C3AI, delivers a keynote at C3 Transform 2022 announcing Version 8, a complete re-architecture of the C3 AI Platform and its prebuilt AI applications. Version 8 empowers enterprises to develop, deploy, and operationalize AI at scale with a new, simplified, cohesive application platform that remains the same across users, from developers and data scientists to business users and citizen data scientists. Version 8 also provides users with a family of eight application Suites — Reliability, Supply Chain, AI CRM, Sustainability, Defense & Intelligence, Oil and Gas, Public Sector, and Financial Services — that each contain a group of specialized, pre-built applications. Learn more about the C3 AI Platform: https://c3.ai/c3-ai-application-platform/ Learn more about C3 AI: https://www.c3.ai Subscribe and turn on channel notifications to hear about the latest updates from C3 AI.Docker swarm and volumes Helpful? Please support me on Patreon: https://www.patreon.com/roelvandepaar With thanks & praise to God, and with thanks to the many people who have made this project possible! | Content (except music & images) licensed under CC BY-SA https://meta.stackexchange.com/help/licensing | Music: https://www.bensound.com/licensing | Images: https://stocksnap.io/license & others | With thanks to user Harry Traynor (serverfault.com/users/382257), user Cedric H. (serverfault.com/users/54121), user BMitch (serverfault.com/users/351549), user 030 (serverfault.com/users/215599), and the Stack Exchange Network (serverfault.com/questions/806649). Trademarks are property of their respective owners. Disclaimer: All information is provided "AS IS" without warranty of any kind. You are responsible for your own actions. Please contact me if anything is amiss at Roel D.OT VandePaar A.T gmail.comDescription: Java doesn’t work well in a container on Kubernetes right? Too big? Too slow to start? Not anymore with Quarkus! Quarkus significantly reduces the container resource requirements for memory and startup, while still supporting standard APIs like Eclipse MicroProfile. Bio: I'm a professional Java Developer working in the software development industry for more than 10 years. Mostly working with Enterprise technologies, I'm involved with the community to help other individuals to spread the knowledge with my blog, my YouTube channel, and with OSS contributions.Лекция 8 | Курс: Вероятностно проверяемые доказательства | Лектор: Дмитрий Ицыксон | Организаторы: Computer Science клуб при ПОМИ РАН Смотрите это видео на Лекториуме: https://lektorium.tv/lecture/14116 Подписывайтесь на канал: https://www.lektorium.tv/ZJA Следите за новостями: https://vk.com/openlektorium https://www.facebook.com/openlektoriumIn this tutorial we'll define a JSON definition file and upload it to Nimbus. Cogniteam: https://www.cogniteam.com/ Nimbus: https://cognimbus.com/Vídeo simples apresentando implementação simples de Map Reduce usando container do Hadoop no Docker. Repositório do projeto: https://github.com/Arekushi/word-count-map-reduce Vídeo do Lucas: https://www.youtube.com/watch?v=Z6CtFGa2LIg Observação: Guardado, na qual me referi algumas vezes, é meu professor da disciplina de Arquitetura de Big Data e DW BI. O vídeo foi construído em cima de uma atividade que exigia o uso de Map Reduce.