Once you have a working model, you store it in a container field in FileMaker. The model will not learn and improve itself from your FileMaker data by running it. In FileMaker, you can use such a model, but not create or modify it. The output of the model has to be text or numbers, but not complex data types or images.
Mainly FileMaker lets you classify images and text. Note that not all models that you will find will work in FileMaker since FileMaker supports only a subset of everything that Core ML can do. It is likely, however, that you will only get the best result for your specific use when you create and train your own model.
You can find some pre-built models to test with. Creating and training a model is not a trivial undertaking, and often, you will want to look for a pre-built model to use. Apple has a lot of documentation on how to create such a model, or convert models from other environments to the Core ML format. First, you need a machine learning model that is fully trained to do its job. It is partially compatible with server-side script execution in that it works only if the server is macOS.
This particular feature does not work on Windows, and it is also not compatible with WebDirect, Data API, or other APIs. If your app is deployed on Apple hardware, then you can use the macOS and iOS/iPadOS native Core ML features with FileMaker Pro and Go. And of course, there is the cost: some of those online APIs are not free, whereas using the capabilities of the OS is. Perhaps there are security constraints that do not allow your data to travel outside of your application. Or you want something that is as fast as possible, and running the routine locally is the only acceptable answer. Perhaps your app needs to be fully self-contained and cannot reach out to the internet. Here at Soliant, we are heavily into Amazon Web Services integrating with image and text recognition through Rekognition and Textract, to name just two, are second nature to us.īut there are good reasons why you could not or would not want to use any of these online services. Many online APIs are available and have been demonstrated in past Developer Conferences and discussed in blog posts. Machine Learning integrations are certainly not new in the FileMaker community. For instance, it is used to scan emails and determine which ones are spam or to detect objects in pictures and video. An ML model is built on a set of known training data so that it can then make predictions about what is in other similar data. Machine Learning or ML falls under the broad umbrella of Artificial Intelligence. MacOS, iOS, and iPadOS have built-in capabilities that can execute Machine Learning models on FileMaker Pro and Go.
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