Core ML Explained: Apple's Machine Learning Framework
This articles will help you to assess and judge the main features of More ML and how you can leverage machine learning in your apps.
Let’s have a look at Core ML, Apple’s machine learning framework.
It is the foundational framework built to provide optimized performance through leveraging CPU, GPU and neural engines with minimal memory and power consumption. The Core ML APIs can be used across Apple's platforms and can supercharge apps with intelligent abilities.
Here is a brief timeline of the evolution of Core ML:
- Core ML was introduced in June 2017 during the Worldwide Developer Conference (WWDC) and emphasized user privacy by focussing on on-device machine learning over other cloud-based solutions from the competition. In December 2017 Google released a toolchain to convert TensorFlow models to Core ML file formats.
- In 2018 Apple released Core ML 2 at WWDC, improving model sizes, speed and most importantly the ability to create custom Core ML models.
- Core ML 3 was released in 2019 and added support for on-device machine learning model training as well as the Create ML desktop app to support custom model training with a GUI for even lower threshold to enter the world of creating custom machine learning models.
Now, let's have a closer look at the features of Core ML. Become a free member or log in to proceed.