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Machine Learning CoreML support is probably one of the most exciting new features in FileMaker 19.
The CoreML framework enables developers to leverage the power of machine learning in FileMaker without any additional plugins or integrations.
Even if you have never worked with machine learning before, Claris significantly simplifies the whole process by facilitating the integration with the new Configure Machine Learning Model script step and ComputeModel function.
That said, don’t let the simplicity of the process fool you: machine learning is an extremely powerful tool that can enhance any FileMaker application.
In this blog post, we’ll review the basic concepts of Machine Learning, how to use pre-trained models, and even train your own CoreML model that can be easily integrated in FileMaker 19 applications!
For everyone who is as excited about CoreML support as we are, we included a sample file that shows how to create and integrate an image recognition CoreML model in FileMaker 19.
Machine Learning in a Nutshell
Arthur Samuel introduced the phrase “Machine Learning” in 1959, defining it as “the ability to learn without being explicitly programmed.”
Unlike the typical program that needs explicit instructions in order to do anything, Machine Learning Models need lots of data and time to train in order to start making predictions.
The more data you give your model, the more accurate results you’ll get.
This is how an iOS app can tell you what plant is on the photo or websites like Zillow predict the prices of the houses.
Image Recognition is a great example of machine learning.
Using CoreML, you can submit an image to a Machine Learning model and get text identifiers in return, like so:
[Read more on the blog ...]
