Machine Learning with Core ML 2 and Swift 5 Learn how to integrate machine learning into your apps. Hands-on Swift 5 coding using CoreML 2, Vision, NLP and CreateML What you'll learn Description ** A practical and concise Core ML 2 course you can complete in less than three hours ** Extra Bonus: Free e-book version included (sells for $28.80 on Amazon)! Wouldn't it be great to integrate features like synthetic vision, natural language processing, or sentiment analysis into your apps? In this course, I teach you how to unleash the power of machine learning using Apple Core ML 2. I'll show you how to train and deploy models for natural language and visual recognition using Create ML. I'm going to familiarize you with common machine learning tasks.

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.

The artificial intelligence strategy from Apple is one that is centered on running workloads locally on devices instead of using cloud-based resources. This positions the approach taken by Apple as one that is different from its core competitors: Google, Amazon, and Microsoft. Here a notable difference on the paths to artificial intelligence technology is that Apple's Create ML software does not work on Windows PCs and, distinctively, it does not use cloud servers. Further in contrast, Google's ML Kit software allows developers add artificial intelligence to Android and iOS apps, yet Apple's comparable Core ML still only works with Apple's own devices. According to CNBC's analysis the avoidance of the cloud is in keeping with Apple's sectoral focus, which is with selling devices.

At WWDC Apple released Core ML 2: a new version of their machine learning SDK for iOS devices. The new release of Core ML, whose first version was released in June 2017, should create an inference time speedup of 30% for apps developed using Core ML 2. They achieve this using two techniques call "batch prediction" and "quantization". Batch prediction refers to the practice of predicting for multiple inputs at the same time (e.g. Quantization is the practice of representing weights and activation in fewer bits during inference than during training. During training, you can use floating-point numbers used for weights and activations, but they slow down computation a lot during inference on non-GPU devices.

During its annual WWDC event, Apple announced the launch of its CreateML tool alongside the sequel of its Core ML framework. CreateML aims to simplify the creation of AI models. In fact, because it's built in Swift, it's possible to use drag-and-drop programming interfaces like Xcode Playgrounds to train models. Core ML, Apple's machine learning framework, was first introduced at WWDC last year. This year, the company has focused on making it leaner and meaner.

At Apple's Worldwide Developers Conference 2018, the Cupertino company announced Core ML 2, a new version of its machine learning software development kit (SDK) for iOS devices. But it's not the only game in town -- just a few months ago, Google announced ML Kit, a cross-platform AI SDK for both iOS and Android devices. Both toolkits aim to ease the development burden of optimizing large AI models and datasets for mobile apps. So how are they different? Apple's Core ML debuted in June 2017 as a no-frills way for developers to integrate trained machine learning models into their iOS, macOS, and tvOS apps; trained models are loaded into Apple's Xcode development environment and packaged in an app bundle.

Artificial intelligence (AI) breakthroughs continue to be infused in developer tooling, with a new machine learning framework for the upcoming iOS 12 being the latest example. At the ongoing 2018 Apple Worldwide Developers Conference in San Jose, Calif., the company introduced the new Core ML 2 framework, which can be used by developers in a variety of ways across a variety of applications on Apple's flagship mobile OS, available now in preview to members of the $100-per-year Apple Developer Program and later this month in a public beta and coming to all in a fall device software update. Apple said the framework allows for easy integration of machine learning models, helping developers build intelligent apps with a minimum of code. "In addition to supporting extensive deep learning with over 30 layer types, it also supports standard models such as tree ensembles, SVMs, and generalized linear models," Apple said. "Because it's built on top of low level technologies like Metal and Accelerate, Core ML seamlessly takes advantage of the CPU and GPU to provide maximum performance and efficiency. You can run machine learning models on the device so data doesn't need to leave the device to be analyzed."

At Apple's Worldwide Developers Conference 2018, the Cupertino company announced Core ML 2, a new version of its machine learning software development kit (SDK) for iOS devices. But it's not the only game in town -- just a few months ago, Google announced ML Kit, a cross-platform AI SDK for both iOS and Android devices. Both toolkits aim to ease the development burden of optimizing large AI models and datasets for mobile apps. So how are they different? Apple's Core ML debuted in June 2017 as a no-frills way for developers to integrate trained machine learning models into their iOS apps.

Apple announced Core ML 2, a new version of its suite of machine learning apps for iOS devices, at the Worldwide Developers Conference (WWDC) 2018 in San Jose, California today. Core ML 2 is 30 percent faster, Apple says, thanks to a technique called batch prediction. Furthermore, Apple said the toolkit will let developers shrink the size of trained machine learning models by up to 75 percent through quantization. Apple also announced Create ML, a new GPU-accelerated tool for native AI model training on Macs. The tool supports vision and natural language, as well as custom data.