Samsung Internet Monthly Meetup - April


The Samsung Internet team is excited to announce April's monthly meetup. Join us to learn and discuss topics about the web.


  • 18:00 GMT+1 Welcome and Introduction - Latest news about Samsung Internet
  • 18:30 GMT+1 Parallel computing in frontend architecture! by Majid Hajian
  • 19:15 GMT +1 Infrastructure for Machine Learning by Natalie Pistunovich
  • 20:00 GMT+1 Q&A

=> Parallel computing in frontend architecture!

A user is working with your application when suddenly, the UI freezes and probably, one of the CPU cores is burning! Sounds like a scary movie? This is your application that cannot leverage modern APIs to lift heavy computation to a different thread where consequently the user suffers the pain. Even though JavaScript is a single-threaded programming language, modern features like Web Workers, WebAssembly, Worklets, and Service Worker allow us to leverage multithreading computing to run tasks parallelly which at the end, makes the user feel like in a rainbow paradise! In this session, I am going to demonstrate my practical (seriously no theory!) experience running jobs in parallel for a JavaScript application that will provide a pleasant user experience and exciting development.

About Majid Hajian:

Majid is a passionate software developer with years of developing and architecting complex web and mobile applications. He is passionate about web platforms, especially Flutter, IoT, PWAs, and performance. He loves sharing his knowledge with the community by writing and speaking, contributing to open source, and organizing meetups and events. Majid is the award-winning author of "Progressive Web App with Angular" book by Apress and "Progressive Web Apps" video course by PacktPub and Udemy. He is (co)organizer of a few mobile and web meetups in Norway as well as Nordic conferences for mobile and Angular, Mobile Era, and ngVikings.

=> Infraestructure for Machine Learning

TensorFlow 2.0 is the new version of the end-to-end open source platform for Machine Learning, where researchers can push the state-of-the-art in ML and developers can build and deploy ML and AI powered intermediate applications. But the ML code, that is at the heart of an ML system in production, usually accounts for a few percents of the entire codebase. In this talk, Natalie will share from her experience the infrastructure side of things, and will discuss considerations in preparing the infrastructure for an ML training, real time and offline, and present a walkthrough using a production example.

About Natalie Pistunovich

Natalie Pistunovich is a learner, a Google Developer Expert for Go, a public speaker, and a sailor. When she’s not working on robust systems with Aerospike, she is organizing the GopherCon Europe and Cloud Nein conferences, and the Berlin chapters of the Go and Women Techmakers user groups. Prior to that, she was an Engineering Manager, Software and Hardware Engineer, and a Co-Founder of a mobile start-up. In her free time, she is wondering if there is life on Mars.