Ever wondered what it takes to implement machine learning in your app? Look no further! In this talk I will suggest different ways to approach this. I am going to compare cloud-based services with local (on-device) machine learning, focusing mostly on the latter. On-device predictions are happening strictly on a mobile device, giving us the benefit of keeping the user’s data private and not depending on the network connection. Features like Google Lens Suggestions and Google Call Screening are all leveraging on-device ML. However the ML models should be prepared and optimized for efficiency and performance on mobile – I’m going to talk about that as well. We’re going to dive into TensorFlow Lite and Firebase MLKit SDKs with some code examples in Kotlin. After attending this talk you will understand the capabilities and limitations of each of these frameworks. You will have a good idea of where to start and what is necessary to implement your idea using ML in your app.