In a previous post I mentioned that I will be sharing some notes about my journey with doing data science and machine learning by Apple technology. This is the firsts of those posts and here I will go about what Core ML is…
Core ML is a computer framework. So what is a framework? Well, in computer terms is a software abstraction that enables generic functionality to be modified as required by the user to transform it into software for specific purposes to enable the development of a system or even a humble project.
So Core ML is an Apple provided framework to speed apps that use trained machine learning models. Notice that word in bold – trained – is part of the description of the framework. This means that the model has to be developed externally with appropriate training data for the specific project in mind. For instance if you are interested in building a classifier that distinguishes cats from cars, then you need to train the model with lots of cat and car images.
As it stands Core ML supports a variety of machine learning models, from generalised linear models (GLMs for short) to neural nets. Furthermore it helps with the tests of adding the trained machine learning model to your application by automatically creating a custom programmatic interface that supplies an APU to your model. All this within the comfort of Xcode!
There is an important point to remember. The model has to be developed externally from Core ML, in other words you may want to use your favourite machine learning framework (that word again), computer language and environment to cover the different aspects of the data science workflow. You can read more in that in Chapter 3 of my “Data Science and Analytics with Python” book. So whether you use Scikit-learnm, Keras or Caffe, the model you develop has to be trained (tested and evaluated) beforehand. Once you are ready, then Core ML will support you in bringing it to the masses via your app.
As mentioned in the Core ML documentation:
Core ML is optimized for on-device performance, which minimizes memory footprint and power consumption. Running strictly on the device ensures the privacy of user data and guarantees that your app remains functional and responsive when a network connection is unavailable.
OK, so in the next few posts we will be using Python and
coreml tools to generate a so-called
.mlmodel file that Xcode can use and deploy. Stay tuned!