# Machine Learning with Amazon Aurora
Machine learning integration with Amazon Aurora currently supports Amazon Comprehend and Amazon SageMaker. Aurora makes direct and secure calls to SageMaker and Comprehend that don’t go through the application layer. Aurora machine learning is based on the familiar SQL programming language, so you don’t need to build custom integrations, move data around, learn separate tools, or have prior machine learning experience.
This lab contains the following tasks:
1. Setup a sample schema and data for ML labs
This lab requires the following prerequisites:
* [Get Started](/prereqs/environment/) (choose **Yes** for the **Enable Aurora ML Labs?** feature option)
* [Connect to the Cloud9 Desktop](/prereqs/connect/)
* [Create a New DB Cluster](/provisioned/create/) (conditional, only if you plan to create a cluster manually)
## 1. Setup a sample schema and data for ML labs
If you have not already opened a terminal window in the Cloud9 desktop in a previous lab, please [following these instructions](/prereqs/connect/) to do so now. Once connected, run the command below, replacing the ==[clusterEndpoint]== placeholder with the cluster endpoint of your DB cluster.
!!! tip "Where do I find the cluster endpoint (or any other placeholder parameters)?"
If you have completed the previous lab, and created the Aurora DB cluster manually, you would find the value of the cluster endpoint on the DB cluster details page in the RDS console, as noted at Step 2. in that lab.
If you are participating in a formal workshop, and the lab environment was provisioned for you using Event Engine, the value of the cluster endpoint may be found on the Team Dashboard in Event Engine.
Otherwise, you can retrieve the cluster endpoint from the CloudFormation stack **Outputs** as indicated in the [Get Started](/prereqs/environment/) prerequisites module.
```
mysql -h[clusterEndpoint] -u$DBUSER -p"$DBPASS"
```
Execute the following SQL queries to create the `mltest` database and tables, and populate them with data. We will use these tables in subsequent labs for inferences.
```
DROP SCHEMA IF EXISTS `mltest`;
CREATE SCHEMA `mltest`;
USE `mltest`;
DROP TABLE IF EXISTS `comments`;
CREATE TABLE `comments` (
`comment_id` INT AUTO_INCREMENT PRIMARY KEY,
`comment_text` VARCHAR(255) NOT NULL
) ENGINE=InnoDB DEFAULT CHARSET=utf8;
INSERT INTO `comments` (comment_text) VALUES
("This is very useful, thank you for writing it!"),
("Awesome, I was waiting for this feature."),
("An interesting write up, please add more details."),
("I don’t like how this was implemented."),
("Horrible writing, I have read better books.");
CREATE TABLE `churn` (
`state` varchar(2048) DEFAULT NULL,
`acc_length` bigint(20) DEFAULT NULL,
`area_code` bigint(20) DEFAULT NULL,
`int_plan` varchar(2048) DEFAULT NULL,
`vmail_plan` varchar(2048) DEFAULT NULL,
`vmail_msg` bigint(20) DEFAULT NULL,
`day_mins` double DEFAULT NULL,
`day_calls` bigint(20) DEFAULT NULL,
`eve_mins` double DEFAULT NULL,
`eve_calls` bigint(20) DEFAULT NULL,
`night_mins` double DEFAULT NULL,
`night_calls` bigint(20) DEFAULT NULL,
`int_mins` double DEFAULT NULL,
`int_calls` bigint(20) DEFAULT NULL,
`cust_service_calls` bigint(20) DEFAULT NULL,
`Churn` varchar(2048) DEFAULT NULL
) ENGINE=InnoDB DEFAULT CHARSET=utf8;
LOAD DATA FROM S3 's3-us-east-1://awsauroralabsmy-us-east-1/samples/mldata/churn.txt'
INTO TABLE `churn`
COLUMNS TERMINATED BY ','
LINES TERMINATED BY '\n'
IGNORE 1 LINES
(state,acc_length,area_code,@dummy1,int_plan,vmail_plan,vmail_msg,day_mins,day_calls,@dummy2,eve_mins,eve_calls,@dummy3,night_mins,night_calls,@dummy4,int_mins,int_calls,@dummy5,cust_service_calls,Churn);
```
Verify that the schema was created correctly by running the following command:
```
SHOW TABLES;
```
You should be able to see two tables, **churn** and **comments**, as shown below. If you don't see the tables rerun the commands.
Disconnect from the DB cluster, using:
```
quit;
```
You may now proceed to the next labs and integrate Aurora with machine learning services.