Syncing data
Keeping data up to date between Airtable and aito.ai ensures you always predict from the latest dataset.
The training of aito.ai machine learning can be started with the "Train model" button at the bottom of the extension.
The view will guide you through the upload, but the recommendation is to create a grid view that contains the training data and name it clearly. In aito.ai extension, you can then choose this view as your training source.
A few tips below.
Use view filters to choose the records you want to upload. The general advice is to create a separate view for aito.ai upload purposes, that controls the training data, and contains filters that remove the data that has empty values.
The extension will automatically create a table name in aito.ai, based on your Airtable table id. This is non-editable at the moment. It looks something like this example: airtable-tblre7vxZNhkv2kzw. You can review the table contents and schema through Console.
If there is existing dataset in aito.ai with the same tablename, the previous data will be deleted and new data will be uploaded.
A word about data synchronisation
Training data is not automatically synchronized to your aito.ai instance. If your training data changes and you want your predictions to be informed by the updates then you can re-upload the new training.
While most of the Airtable field types are supported by aito.ai, there are some limitations. If you view has columns such as buttons and images, aito.ai will automatically ignore them in the upload. You will see the list of included and excluded fields in the upload view.
Linked fields (either externally, or within the table) are automatically added to aito.ai. In this case, multiple tables will also be created in aito.ai, capturing your original data structure. The neat feature here is that then the data behind the links is also used for your predictions.
We recommend you to be careful with any personal information. It is not recommended to include fields that might cause biased predictions. For example, if you are dealing with people, always exclude their demographics such as sex or religion from the training data.
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