NJP-2576 Introducing Machine Learning to the JavaScript Toolchain | Voxxed Days

Voxxed Days Zurich 2019
on Tuesday 19 March

   Introducing Machine Learning to the JavaScript Toolchain

Conference

Frontend and UX
Frontend and UX
Intermediate level

Websites are slow! Double-click shows that the average load time on a 3G network is 19 seconds! On top of that, on mobile devices, JavaScript compared to a JPG image with the same size may require x25 more processing time.

How to speed up our apps? Lazy-loading is here to help! By only loading the minimum amount of JavaScript during the initial page load we can improve the UX dramatically. However, this brings another set of questions - how to decide what to load lazily, how to provide instant page load by mindfully prefetching the bundles, without draining the users’ mobile data plan?

Machine learning is already playing an essential role in our day-to-day life, and it has the potential to assist in our development process for smaller, smarter, and faster JavaScript applications!

In this talk, we'll see how we can create a machine learning model from a Google Analytics report. Later, by empowering static analysis techniques, we'll map this model to the lazy-loaded JavaScript chunks and apply predictive prefetching and data-driven bundling. In the last part of the presentation, we'll look at Guess.js which provides a sample implementation of these ideas.

Javascript   Machine Learning for Developers   JS framework  
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Minko Gechev
Minko Gechev
From Google

Minko is an engineer in the Angular team at Google. He loves to experiment with abstract theoretical computer science concepts transform them into robust industrial solutions. Minko’s working on compilers, tools for static code analysis, and machine learning. Some his projects are the book “Switching to Angular”, Guess.js, codelyzer, the official Angular style guide, the Go linter revive, and many others.


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