Predictive AI Layers for Databases

AI Tables

AI-Tables differ from normal tables in that they can generate predictions upon being queried and returning such predictions as if it was data that existed in the table. Simply put, an AI-Table allows you to use machine learning models as if they were normal database tables, in something that in plain SQL looks like this:

SELECT <predicted_variable> FROM <ML_model> WHERE <conditions>

Automated Machine Learning and AI Tables

Automated machine learning (AutoML) makes the complex Machine Learning process from Data Acquisition to making a Prediction simple. All the steps in between are abstracted by an AutoML platform.

How predictive AI layers work

The whole solution consists of two important parts:

  1. The Machine Learning models are exposed as database tables (AI-Tables) that can be queried with the SELECT statements.
  2. The ML model generation and training are done through a simple INSERT statement.

The Example of Predictive AI Layers

Imagine that you want to solve the problem of estimating the right price for a car on your website that has been selling used cars over the past 2 years.

INSERT INTO
mindsdb.predictors(name, predict, select_data_query)
VALUES
('used_cars_model', 'price', 'SELECT * FROM used_cars_data);
SELECT price,
confidence
FROM mindsdb.used_cars_model
WHERE model = "a6"
AND mileage = 36203
AND transmission = "automatic"
AND fueltype = "diesel"
AND mpg = "64.2"
AND enginesize = 2
AND year = 2016
AND tax = 20;

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