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The big thing in Aito is still, that it is 'modelless'. You don't need a prepared model to do a prediction, because the model is done ad hoc / lazily for the query. It is a much easier workflow, because you don't need to care about the models and you can have very free format / templated queries like this:

  predictCaseFieldForCustomer(customer, department, case, predictedField) {
    return aito.query({
      from: f'customer-$customer',
      where: {
        $on : [
          { case : case }, 
          { department : department }
        ]
      },
      predict : predictedField
    })
  }



Yep, that's dependent and independent variables in a simple linear regression (SLR). Postgres has SLR built in. Madlib is an extension to Postgres that does much more.


Please correct me if I'm mistake, but doesn't that require a separately prepared model in both Postgres and madlib?

You cannot simply request for an arbitrary prediction in a single & simple query.




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