Day 2 - Wednesday, 28 October
9:30 AM PT
Zero to app: livecoding a cross platform app with Firebase and Flutter
Coding an app for multiple platforms has never been easier thanks to cross-platform toolkits like Flutter. Similarly, thanks to backend-as-a-service platforms like Firebase, building a multi-user experience on a secure, serverless, scalable infrastructure can be done quickly and simply. In this talk we'll build an app from scratch that allows you to run live polls of your users. To make this talk even more exciting, while we're building this app, we will run polls from our app for you, our awesome viewers, to interact with live. We're looking forward to all of you coming to join us live to learn something new and cast your votes with Flutter and Firebase.
10:30 AM PT
Iterate your way to a delightful app experience using Firebase Remote Config and Firebase A/B Testing
One of the best ways of delivering a great experience to your users is to change the app dynamically based on each user's attributes and behavior. We'll show you new updates in Firebase that make it easy to customize your app experiences for different groups of your users on the fly, and measure and adjust them dynamically to optimize the user journey.
10:50 AM PT
Serverless security modeling in Firebase
Using secure design principles as our guide, we will cover the default security of Firebase services and what developers need to do to configure and secure their applications. Walking through a simple serverless application built with Firebase backend products, we will discuss different security threats, from malicious actors to user error. As we go, we'll build a checklist that you can use to audit your own app's security and protect yourself and your users.
11:15 AM PT
Adding on-device recommendations to your app using TensorFlow and Firebase
As a developer, ML can help you build better apps. In this session we'll guide you through simple steps to build and train a TensorFlow model that does on-device recommendations based on content that the user has shown interest in. You'll then convert the model to TensorFlow Lite to run on mobile and deploy it on Firebase.