Apple released Core ML at WWDC'17, and it was updated to Core ML 2 this year. As a reminder, Core ML enables developers to integrate machine learning models into iOS and MacOS apps. This was the first big attempt in this field, and initially developers really liked it for several reasons.
Core ML supports a variety of machine learning models, including neural networks, tree ensembles, support vector machines, and generalized linear models. Core ML requires the Core ML model format (models with a .mlmodel file extension).
Apple also provides converters to convert the models created in several other libraries to Core ML format. As we have used these converters in this book, we find that these converters are extremely simple to use and work with most famous existing machine learning libraries.
Apple also provides several popular, open source models that are already in the Core ML model format, which can be directly downloaded and used in building our applications.
Core ML is optimized for on-device performance, which minimizes memory footprint and power consumption. Running strictly on the device also ensures that user data is kept secure, and the app runs even in the absence of a network connection.
Core ML's biggest advantage is that it is extremely simple to use. Just a few lines of code can help you integrate a complete machine learning model. Since the release of Core ML, there has been a flood of innovative projects using it. However, there are limitations around what Core ML can do.
Core ML can only help you integrate pre-trained ML models into your app. So, this means you can do predictions only; no model training is possible.
Thus far, Core ML has proved to be extremely useful for developers. Core ML 2, which was announced at WWDC this year, should improve inference time by 30% using techniques called quantization and batch prediction.
