In the WWDC19 was presented Core ML 3, which is optimized for on-device performance and ensures the privacy by doing all the process locally and not running in any server. The newest version of Core ML provides model flexibility and model personalization. Now, Core ML has support for more than 100 Neural Network layers allowing to import the state of the art models into an app. A new converter from TensorFlow is already in place and a ONNX converter is soon to be released. The model gallery has also been updated.

Imagine the ability to build amazing applications by using State-of-the-Art machine learning models without having to know in-depth machine learning. Welcome to Apple's Core ML 3! Are you an avid Apple fan? Do you use the iPhone? Ever wondered how Apple uses machine learning and deep learning to power its applications and software? If you answered yes to any of these questions – you're in for a treat! Because in this article, we will be building an application for the iPhone using deep learning and Apple's Core ML 3. Here's a quick look at the app: Software developers, programmers, and even data scientists love Apple's AI ecosystem.

Though it didn't get a ton of stage time during the keynote at this year's WWDC, there's a lot to be excited about in the latest iteration of Apple's machine learning framework, Core ML 3. In this post, I'll highlight the biggest changes to the software and discuss their implications for developers and machine learning engineers. The biggest addition to Core ML is the introduction of on-device training. Prior to this release, Core ML supported inference only. Training was done server-side with a framework like TensorFlow or PyTorch, and models were converted to Core ML in order to make predictions in an app. Core ML 3 changes that, becoming the first widely available machine learning framework to support both inference and training directly on-device.

To say this year has been massive for API and framework updates would be underselling WWDC 2019. Core ML has been no exception. So far with Core ML 2 we saw some amazing updates and that made on device training amazingly simple. However, there was still a lot to be desired and if you wanted to implement newer models like YOLO you needed to drop down to Metal and do a lot of leg work to get a model up and running. Now we have Core ML 3 and honestly outside of optimization alone I'm not too sure why you would need to drop down to metal after this new update.