Getting Started

MindsDB integrates with the most popular databases and also with the DBT and MLflow workflow you already have.

^{_{To try MindsDB right away, without bringing your own data or model, check out our Quick Start Guide.}}

1. Choose your MindsDB installation path.

   MindsDB CloudDocker

   Create your free MindsDB Cloud account.

   

   To get started with a Docker installation, begin with our Docker instructions.

2. Open your SQL client and connect to MindsDB.

   ^{_{If you do not already have a preferred SQL client, we recommend DBeaver Community Edition}}

   MindsDB CloudDocker

   1. Create a new MySQL connection.

      

   2. Configure it using the following parameters, as well as the username and password you created above:

          Host: mysql.mindsdb.com
          Port: 3306
          Database: mindsdb
      

      

   1. Create a new MySQL connection.

      

   2. Configure it using the following parameters. Password remains empty.

          Host: localhost
          Port: 47335
          Database: mindsdb
          Username: mindsdb
      

      

3. Connect your data to MindsDB using the CREATE DATABASE syntax.

   ^{_{Example taken from our Quick Start Guide.}}

   

4. You can now preview the available data with a standard SELECT.

   ^{_{Example taken from our Quick Start Guide.}}

   

5. Now you are ready to create your model, using the CREATE PREDICTOR syntax. If you already have a model in MLFlow, you can connect to your model as well.

   MindsDB is creating my modelMy model is in MLflow

   Example taken from our Quick Start Guide.

   

   Example taken from our Docker Guide.

   mysql> CREATE PREDICTOR mindsdb.home_rentals_predictor
       -> FROM example_data (select * from demo_data.home_rentals)
       -> PREDICT rental_price
       -> USING url.predict='http://host.docker.internal:1234/invocations',
       -> format='mlflow',
       -> dtype_dict={"alcohol": "integer", "chlorides": "integer", "citric acid": "integer", "density": "integer", "fixed acidity": "integer", "free sulfur dioxide": "integer", "pH": "integer", "residual sugar": "integer", "sulphates": "integer", "total sulfur dioxide": "integer", "volatile acidity": "integer"};
   Query OK, 0 rows affected (0.21 sec)
   

6. The SELECT syntax will allow you to make a prediction based on features.

   ^{_{Example taken from our Quick Start Guide.}}

   

7. To integrate your predictions into your DBT workflow, you will need to make four changes:

   profiles.ymlschema.ymlpredicted_rentals.sqldbt_project.yml

   mindsdb:
       type: mysql
       host: mysql.mindsdb.com
       user: mindsdb.user@example.com
       password: mindsdbpassword
       port: 3306
       dbname: mindsdb
       schema: example_data
       threads: 1
       keepalives_idle: 0 # default 0, indicating the system default
       connect_timeout: 10 # default 10 seconds
   

   version: 2
   
   models:
         - name: predicted_rentals
           description: "Integrating MindsDB predictions and historical   data"
   

   with predictions as (
       SELECT hrp.rental_price as predicted_price, hr.rental_price as actual_price
       FROM mindsdb.home_rentals_predictor hrp
       JOIN exampleData.demo_data.home_rentals hr
       WHERE hr.number_of_bathrooms=2 AND hr.sqft=1000;
   )
   select * from predictions;
   

   models:
       home_rentals:
           +materialized: view