go

Recursos de programación de go
Google Cloud, in partnership with Codemotion, is proud to release this unique online training program, available for free, to help developers learn about the services in Google Cloud Platform and show them how to get the most out of each service. Get more info and apply here: http://bit.ly/GCPDevs What is GCP? Google Cloud Platform is a suite of cloud computing services offered by Google and based on the same infrastructure that Google uses for its own products, such as Gmail, YouTube or Google Search. Since we are talking about the company that runs the most widely used search engine in the world, the ability to tap into its own infrastructure represents a guarantee of reliability and scalability. As an extra added value, Google has always focused on innovation and has had to develop new technologies over time to meet unique requirements in terms of volume of data and the capacity needed to process it. Take a quick look at all the benefits you can receive using GCP. About GCP Developer Enablement Program The course is aimed at everyone who has already chosen GCP, but also at those who are yet to make that choice, or anyone who is simply interested in the platform and what it has to offer. The program consists of 3 independent courses, where two Google Cloud Certified trainers go into the basics of the platform and its offering, through a combination of presentations, demos, and case studies. Course 1: Core Intro to GCP: What is it? What are its advantages and main services? Course 2: Big Data What’s the offering for Big Data and Machine Learning on GCP? Course 3: Kubernetes Orchestrating containers with Kubernetes GCP Test your knowledge at the end of each course with an in-depth related quiz. What can I expect on completion of the program? Once you have completed all the courses in this learning program and have successfully passed the related quizzes, you will receive: - a certificate of online attendance and a digital badge that you can share on your social networks and your LinkedIn profile - 100 credits on the Qwiklabs platform, that give you access to up to 5 courses for a deeper understanding of GCP - a free course on Google Cloud with Coursera For any further info feel free to contact us at training@codemotion.it Get more info and apply here: http://bit.ly/GCPDevs
VP of Engineering at Ebury Victor is the VP of Engineering for Ebury, comes from managing international software development teams creating operating systems and applications for Mobiles and Cloud solutions. More recently worked as Director of Engineering at Bitnami (YC'13) and VP of Commercial Engineering at Canonical (sponsors of Ubuntu Linux). Victor is passionate about System Reliability and he is a go and Kubernetes enthusiast.
Software Engineer at Google Jaana B. Dogan works on making Google production services more monitorable and debuggable. Previously, she worked on the Go programming language at Google and has a decade-long experience in building developer platforms and tools.
Novedades en el mundo del desarrollo. Primer streaming de "#laFunciónCodelyTV()" con: * Dominios io en peligro * KPIs para equipos de desarrollo * Novedades de la #WWDC * #GitHub Desktop 2.0 * Normativa de pagos online #SCA * Machine Learning aplicado a código con #FacebookAroma * Sorpresitas varias 🙂 🔗 Enlaces relacionados: 🎓 Cursos CodelyTV Pro: |-- 💸 Oferta lanzamiento curso Go: http://bit.ly/oferta-go |-- 🐨 Curso "Introducción a Go: Tu primera app": https://bit.ly/go-codelytv |-- 📕 Catálogo cursos: https://bit.ly/cursos-codely {▶️} CodelyTV |-- 🎥 Suscríbete a nuestro canal: https://www.youtube.com/c/CodelyTV?sub_confirmation=1 |-- 𝐟 Facebook: https://facebook.com/CodelyTV/ |-- 📸 Instagram: https://instagram.com/CodelyTV/ |-- 🐦 Twitter: https://twitter.com/CodelyTV
Complexity in systems should be defeated if it is possible to do. But the default nature of our computer systems are complex and servers are doomed to fail. In this talk, we will go through new approaches in modern architectures to design and evaluate new computer systems.
Thinking of moving to Microservices? Watch out! That quest is full of traps, social traps. If you are not able to handle it, you may be blocked by meetings, frustration, endless challenges that will make you miss the monolith. In this talk, I share my experience and mistakes, so you can avoid them. Creating or migrating to a Microservices architecture might easily become a big mess, not only due to technical challenges but mostly because of human factors: it’s a major change in the software culture of a company. In this talk, I’ll share my past experience as the technical lead of an ambitious Microservices-based product, I’ll go through the parts we struggled with, and give you some advice on how to deal with what I call the Six Pitfalls: The Common Patterns Phobia The Book Club Cult The Never-Decoupled Story The Buzz Words Syndrome The Agile Trap The Conway’s Law Hackers
Reinforcement Learning is a hot topic in Artificial Intelligence (AI) at the moment with the most prominent example of AlphaGo Zero. It shifted the boundaries of what was believed to be possible with AI. In this talk, we will have a look into Reinforcement Learning and its implementation. Reinforcement Learning is a class of algorithms which trains an agent to act optimally in an environment. The most prominent example is AlphaGo Zero, where the agent is trained to place tokens on the board of Go in order to win the game. AlphaGo Zero has won against the world champion which was thought to be impossible at that time. This was enabled by combining Reinforcement Learning with Deep Neural Networks and is today known as Deep Reinforcement Learning. This has shifted the frontier of Artificial Intelligence and enabled multiple complex use cases, among them controlling the cooling devices in the server rooms by google. Applying Deep Reinforcement Learning saved them several million in power costs. In this talk, we will understand the basics of Deep Reinforcement Learning and implement a simple example. We will have a look at OpenAIs gym which is the defacto standard for Reinforcement Learning environments. This will enable the audience to implement both an environment and Reinforcement Learning agent on their own.
We have been celebrating 2018 as the Year of the Service Mesh, where an open source effort known as Istio has taken and changed how we design and release our applications.​ ​​​​​​As we start to go toward cloud-native infrastructure and build our applications out of microservices, we must fully face the drawbacks and challenges to doing so. Some of these challenges include how to consistently monitor and collect statistics, tracing, and another telemetry, how to add resiliency in the face of unexpected failure, how to do powerful feature routing and much more. Istio and service mesh in general help developers solve this in a non-invasive way. In this session, we’ll show how you can take advantage of these capabilities in an incremental way. We expect most developers haven’t adequately solved for these issues, so we’ll take it to step by step and build up a strong understanding of Istio, how to get quick wins, and harness its power in your production services architecture.
“Serverless” is the hottest ticket in town right now. But many serverless platforms restrict your choice of language and / or dictate where your code runs. In this talk, I’ll describe how we can go to the serverless ball with open source and the Fn project in particular. “Serverless” aims to improve developer productivity by abstracting, underlying infrastructure layers. The servers are still there, but you just can’t see them. This abstraction allows the developer to focus solely on the functions that deliver value to the business and not on the plumbing. The economics of serverless are also interesting since you only consume resources when your functions run, rather than having applications running continually waiting to server requests. Sadly some leading serverless platforms are not open and restrict choice in terms of: - language - deployment In this talk, I want to show how you can do serverless development with your choice of language, and deployment location. Presentation Summary The evolution of “serverless” Functions as a Service (FaaS) open source serverless frameworks The Fn project (see http://fnproject.io ) Fn functions building managing state logging FDKs (Function Development Kits) How we can link individual functions together to create serverless applications Building an example serverless application with Fn.
Did you try to implement one of the new java.util.concurrent.Flow.* interfaces yourself? Then you’re most probably doing it wrong. The purpose of this talk is to show that implementing them yourself is far from trivial and to discuss the actual reasons why they have been included in the JDK. Reactive Streams is a standard for asynchronous data processing in a streaming fashion with non-blocking backpressure. Starting from Java 9, they have become a part of the JDK in the form of the java.util.concurrent.Flow interfaces. Having the interfaces at hand may tempt you to write your own implementations. Surprising as it may seem, that’s not what they are in the JDK for. In this session, we’re going to go through the basic concepts of reactive stream processing and see how (not) to use the APIs included in JDK 9+. Plus we’re going to ponder the possible directions in which JDK’s Reactive Streams support may go in the future.