Core ML is a machine learning (ML) model format created and supported by Apple that compiles, deploys, and runs on Apple devices. Developers who train their models in popular frameworks such as TensorFlow and PyTorch convert models to Core ML format to deploy them on Apple devices. Neo is an ML model compilation service on AWS that enables you to automatically convert models trained in TensorFlow, PyTorch, MXNet, and other popular frameworks, and optimize them for the target of your choice. With the new automated model conversion to Core ML, Neo now makes it easier to build apps on Apple's platform to convert models from popular libraries like TensorFlow and PyTorch to Core ML format. In this post, we show how to set up automatic model conversion, add a model to your app, and deploy and test your new model.

Core ML, Apple's primary model framework, got a big boost with the inclusion of on-device model training last year. While the hopes for the introduction of on-device training for recurrent neural networks (RNNs) this year were dashed, there were still some pretty interesting announcements. As you can see, we have a new metadata tab that shows the layer distribution for the Core ML model. Most notably, the utility section consists of two new features -- Model Encryption and Model Deployment. Until now, updating models in production apps has been a headache.

Core ML is a Machine Learning Library launched by Apple in WWDC 2017. It allows iOS developers to add real-time, personalized experiences with industry-leading, on-device machine learning models in their apps by using Neural Engine. Apple introduced A11 Bionic Chip with Neural Engine on September 12, 2017. This neural network hardware can perform up to 600 Basic Operations per Second(BOPS) and is used for FaceID, Animoji and other Machine Learning tasks. Developers can take advantage of the neural engine by using Core ML API.

MLModel is the class that encapsulates the model. The Core ML model accepts the inputs and outputs in the form of MLFeatureValue. MLFeatureValue lets us directly use a CGImage. Along with that, we can pass the image constraints for the model. It creates the CVPixelBuffer from the CGImage for you, thereby avoiding the need to write helper methods.

Before we jump in, a few words about MakeML. The app runs on macOS 10.14 . It helps you to create object detection Core ML Models without writing a line of code. The app manages Python dependencies, data preparation, and visualizes the training process. I'm going to show you how I trained an object detection Core ML model that can identify my guitar and amplifier with the use of the MakeML App.

IBM Watson NLC and Conversation services (as well as many other NLU cloud platforms) provide a Swift SDK to use in custom apps to implement intent understanding from natural language utterances. These SDKs and the corresponding NLU platforms are super powerful. They provide much more than simply intent understanding capability -- they also detect entities/slots and provide tools to manage complex, long running conversation dialogs. However, even for the most basic NLC inference, these SDKs depend on network connectivity, as the NLC model is run in the Cloud. By using Core ML models to run NLC and NLU algorithms on the device, we can provide similar functionality without relying on cloud inference.

If you've been following Apple's announcements from the past year, you know that they are heavily invested in machine learning. Ever since they introduced Core ML last year at WWDC 2017, there are tons of apps which have sprung up which harness the power of machine learning. However, one challenge developers always faced was how to create the models? Luckily, Apple solved our question last winter when they announced the acquisition on Turi Create from GraphLab. Turi Create is Apple's tool which can help developers simplify the creation of their own custom models. With Turi Create, you can build your own custom machine learning models.

So you've made this great social media app and you are about to sit back and wait for the money to roll in. But, there is a problem: people keep trying to upload nude photos to it. What if we could have a trained machine learning model that could detect not-safe-for-work (NSFW) content and do it on an iOS device before any image would be uploaded to a server? Developing this trained machine learning model is way out of scope for this blog post. Luckily, the good people at Yahoo! have already done this with their open-sourced trained Caffe models.

Core ML makes it easy for iOS developers to add deep machine learning to their apps. In this post, I'll show you how you can train a Core ML model to derive intelligent insights. Machine learning has undoubtedly been one of the hottest topics over the past year, with companies of all kinds trying to make their products more intelligent to improve user experiences and differentiate their offerings. Google invested between $20B and $30B in artificial intelligence just last year alone, according to McKinsey's State Of Machine Learning And AI, 2017. AI is turning into a race for patents and intellectual property (IP) among the world's leading tech companies...The report cites many examples of internal development including Amazon's investments in robotics and speech recognition, and Salesforce on virtual agents and machine learning.

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