Android Developers Blog

Posted by Hoi Lam , Android Machine Learning This blog post is part of a weekly series for #11WeeksOfAndroid. Each week we’re diving into a key area of Android so you don’t miss anything. Throughout this week, we covered various aspects of Android on-device machine learning (ML). Whichever stage of development be it starting out or an established app; whatever role you play in design, product and engineering; whatever your skill level from beginner to experts, we have a wide range of ML tools for you. Design - ML as a differentiator “Focus on the user and all else will follow” is a Google mantra that becomes even more relevant in our machine learning age. Our Design Advocate, Di Dang, highlighted the importance of finding the unique intersection of user problems and ML strengths . Too often, teams are so keen on the idea of machine learning that they lose sight of their user needs. Di outlined how the People + AI Guidebook can help you make ML product decisions and used the example of...

Posted by Hoi Lam , Android Machine Learning Yesterday, we talked about turnkey machine learning (ML) solutions with ML Kit . But what if that doesn’t completely address your needs and you need to tweak it a little? Today, we will discuss how to find alternative models, and how to train and use custom ML models in your Android app. Find alternative ML models Crop disease models from the wider research community available on tfhub.dev If the turnkey ML solutions don't suit your needs, TensorFlow Hub should be your first port of call. It is a repository of ML models from Google and the wider research community. The models on the site are ready for use in the cloud, in a web-browser or in an app on-device. For Android developers, the most exciting models are the TensorFlow Lite (TFLite) models that are optimized for mobile. In addition to key vision models such as MobileNet and EfficientNet, the repository also boast models powered by the latest research such as: Wine classificatio...