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...

Posted by Christiaan Prins, Product Manager, ML Kit and Shiyu Hu, Tech Lead Manager, ML Kit Two years ago at I/O 2018 we introduced ML Kit, making it easier for mobile developers to integrate machine learning into your apps. Today, more than 25,000 applications on Android and iOS make use of ML Kit’s features. Now, we are introducing some changes that will make it even easier to use ML Kit. In addition, we have a new feature and a set of improvements we’d like to discuss. A new ML Kit SDK, fully focused on on-device ML ML Kit's APIs are built to help you tackle common challenges in the Vision and Natural Language domains. We make it easy to recognize text, scan barcodes, track and classify objects in real-time, do translation of text, and more. The original version of ML Kit was tightly integrated with Firebase, and we heard from many of you that you wanted more flexibility when implementing it in your apps. As a result, we are now making all the on-device APIs available in a new ...

Posted by Christiaan Prins and Max Gubin Today we are announcing the release of two new features to ML Kit: Language Identification and Smart Reply . You might notice that both of these features are different from our existing APIs that were all focused on image/video processing. Our goal with ML Kit is to offer powerful but simple-to-use APIs to leverage the power of ML, independent of the domain. As such, we are excited to expand ML Kit with solutions for Natural Language Processing (NLP)! NLP is a category of ML that deals with analyzing and generating text, speech, and other kinds of natural language data. We're excited to start out with two APIs: one that helps you identify the language of text, and one that generates reply suggestions in chat applications. Both of these features work fully on-device and are available on the latest version of the ML Kit SDK, on iOS (9.0 and higher) and Android (4.1 and higher). Generate reply suggestions based on previous messages A new feat...