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You can work with audio, and you can even work with text. You can work with video, let's say you want to do credit card detection. You can work with gestures, let's say handwriting detection on the watch. But with Core ML you can do a lot more than just images. So you've already seen an example of an app that used images, the flower predictor. So it's going to be a fun session with a couple demos, let's get started. Finally, we're going to talk about how you can obtain Core ML models for use in all your applications. We're then going to talk about how Core ML is optimized for the hardware on which it runs for or runs on, and what that means for you as a developer. So in this session, we're going to pick up from where we left off and we're going to talk a little bit more about the different kinds of use cases, all the cool stuff you guys can do with Core ML. One line of code to instantiate the model, and one line of code to make a prediction from that model. We saw that in order to use that or build that application, it just took a few lines of code.

It was an application where given a picture, let's say this pink rose, the application is supposed to tell you what kind of a flower it was and how confident it was.

In that session, we also saw a little demo of a flower predictor. Xcode will bundle both the code as well as the model in your app. You write your application code, you build it. You start with a machine learning model, you drag and drop that model into Xcode, Xcode will automatically generate a Swift or an Objective C interface for you to program against that model. Now the first and the most important thing about Core ML is that you can think of your machine learning models just like code, and you interact with them just like any other Swift class. For those of you that missed that session, let's just take a couple moments to recap some of the key things we learned in that session. We had a session on Tuesday that introduced Core ML. It's the easiest way for you to integrate machine learning models in your applications.Ĭore ML is available on macOS, iOS, watchOS, and tvOS. My name is Krishna and I'm from the Core ML Engineering team, and today we're going to talk about Core ML in Depth.