How to manage models - PolarDB - Alibaba Cloud Documentation Center

PolarDB for AI includes built-in large models and also lets you build custom models or load external models using SQL statements. This topic explains how to create, upload, evaluate, and use models for inference.

Model overview

Model category	Description
Built-in models	Directly call built-in large language models (LLMs) using SQL statements to perform complex analysis tasks. You do not need to synchronize data from PolarDB to other AI platforms. For more information, see Data inference and interaction with the Qwen LLM.
Custom models	Built-in LLMs might not meet all your business needs. You can create custom models and tune them for better performance. In addition, PolarDB for AI lets you run models in confidential containers for enhanced data security. The workflow to build a custom model is as follows: Create a model Evaluate the model Perform model inference
External models	If you have an existing model, you can upload and deploy it to PolarDB for inference. Examples include models based on the Sklearn framework, such as the light gradient boosting machine (LightGBM) and GBDT, or deep learning algorithms based on the TensorFlow or PyTorch frameworks. The workflow to load an external model is as follows: Upload the model Deploy the model Evaluate the model Perform model inference

Create a model

Create a machine learning model. Model creation is an asynchronous process. Query the model status to check whether the creation is complete.

Syntax

CREATE MODEL model_name WITH ( model_class = '', x_cols = '', y_cols='',model_parameter=()) AS (SELECT select_expr [, select_expr] ... FROM table_reference)

Parameters

Parameter	Description
model_name	The model name.
model_class	The model type. Valid values: lightgbm: For more information, see LightGBM algorithm. deepfm: For more information, see DeepFM algorithm. kmeans: For more information, see K-means clustering algorithm (K-Means). randomforestreg: For more information, see Random forest regression algorithm (Random Forest Regression). gbrt: For more information, see GBRT algorithm (GBRT). linearreg: For more information, see LR algorithm (Linear Regression). svr: For more information, see Support vector regression algorithm (SVR). bst: For more information, see BST algorithm.
x_cols	The input columns for model creation. Note Separate multiple column names with commas (,).
y_cols	The output column for model creation.
model_parameter	The parameters for model creation. When the value of `model_class` is lightgbm, see metric description for the values of `model_parameter`. When the value of `model_class` is deepfm, see parameter description for the values of `model_parameter`. If `model_class` is set to kmeans, see Parameter description for details about `model_parameter`. When the value of `model_class` is randomforestreg, see Parameter description for the values of `model_parameter`. When the value of `model_class` is gbrt, see Parameter descriptions for the values of `model_parameter`. When the value of `model_class` is `linearreg`, the values for `model_parameter` are described in Parameter description. For information about the `model_parameter` value when `model_class` is svr, see Parameter description. When `model_class` is bst, see Parameter description for the value of `model_parameter`.
select_expr	The column name.
table_reference	The table name.

Example

Create the airlines_gbm model.

/*polar4ai*/CREATE MODEL airlines_gbm WITH (model_class='lightgbm', x_cols ='Airline,Flight,AirportFrom,AirportTo,DayOfWeek,Time,Length', y_cols='Delay',model_parameter=(boosting_type='gbdt', n_estimators=100, max_depth=8, num_leaves=256)) as (SELECT * FROM db4ai.airlines_train)

The following result is returned:

Query OK, 0 rows affected (0.79 sec)

Upload a model

PolarDB for AI supports uploading custom machine learning models. You can upload models that you created offline to PolarDB for AI and then use the platform to manage them. PolarDB for AI supports the following model formats: PMML, ONNX, and Checkpoint (for TensorFlow and PyTorch platforms).

Syntax

UPLOAD MODEL model_name WITH (model_location = '', req_location = '')

Parameters

Parameter	Description
model_name	The model name.
model_location	The URL of the model file.
req_location	The URL of the file that contains the model's runtime dependencies.

Example

Upload the created my_model model to the PolarDB for AI platform.

/*polar4ai*/UPLOAD MODEL my_model WITH (model_location='https://xxxx.oss-cn-hangzhou.aliyuncs.com/xxxx/model.pkl?Expires=xxxx&OSSAccessKeyId=xxxx&Signature=xxxx', req_location='https://xxxx.oss-cn-hangzhou.aliyuncs.com/xxxx/requirements.txt?Expires=xxxx&OSSAccessKeyId=xxxx&Signature=xxxx');

The following result is returned:

Query OK, 0 rows affected (0.29 sec)

Run the following command to check the model status.

/*polar4ai*/ SHOW MODEL my_model;

The following result is returned:

+-------------+-----------------------------------------------------------------------------------------------------------------------------+
| modelStatus | modelPath                                                                                                                   |
+-------------+-----------------------------------------------------------------------------------------------------------------------------+
| saved       | http://db4ai-collie-cn-hangzhou.oss-cn-hangzhou.aliyuncs.com/xxxxx.pkl?OSSAccessKeyId=xxxxxx&Expires=xxxx&Signature=xxxxxx  |
+-------------+-----------------------------------------------------------------------------------------------------------------------------+
1 row in set (0.23 sec)

If modelStatus is saved, the model is uploaded.

Deploy a model

Deploy the uploaded model on the PolarDB for AI platform. After deployment, the model is online, which makes subsequent inference calls faster.

Syntax

DEPLOY MODEL model_name

Parameters

Parameter	Description
model_name	The model name.

Example

Deploy the my_model model on the PolarDB for AI platform.

/*polar4ai*/ DEPLOY MODEL my_model;

The following result is returned:

Query OK, 0 rows affected (0.29 sec)

Run the following command to check the model status:

/*polar4ai*/ SHOW MODEL my_model;

The following result is returned:

+-------------+-----------------------------------------------------------------------------------------------------------------------------+
| modelStatus | modelPath                                                                                                                   |
+-------------+-----------------------------------------------------------------------------------------------------------------------------+
| serving     | http://db4ai-collie-cn-hangzhou.oss-cn-hangzhou.aliyuncs.com/xxxxx.pkl?OSSAccessKeyId=xxxxxx&Expires=xxxx&Signature=xxxxxx  |
+-------------+-----------------------------------------------------------------------------------------------------------------------------+
1 row in set (0.23 sec)

If modelStatus is serving, the model is deployed.

View models

Note

You can view only your custom models and uploaded external models. You cannot view the models that are built into PolarDB for AI.

View the model list

View all custom and uploaded external models in the current cluster.

Syntax

SHOW MODELS

Example

/*polar4ai*/SHOW MODELS

The following result is returned:

+-----------------------+-----------------+--------------+
| model_name            | model_class     | model_status |
+-----------------------+-----------------+--------------+
| airline_rfr           | randomforestreg | saved        |
| gbrt1                 | gbrt            | saved        |
| airline_deepfm        | deepfm          | saved        |
| airlines_gbm          | lightgbm        | saved        |
| lgbm1                 | lightgbm        | saved        |
| blackfriday_linearreg | linearreg       | saved        |
+-----------------------+-----------------+--------------+
6 rows in set (0.24 sec)

View model status

View the current status of a model. After you create a model, use this syntax to check whether the creation is complete. The model can have the following statuses:

training: The model is being created.
loading_data: The model is loading data.
trained: The model is created.
saved: The model is saved.
serving: The model is in service.
deleted: The model is deleted.

Syntax

SHOW MODEL model_name

Parameters

Parameter	Description
model_name	The model name.

Example

View the current status of the airlines_gbm model.

/*polar4ai*/SHOW MODEL airlines_gbm;

The following result is returned:

+-------------+-----------------------------------------------------------------------------------------------------------------------------+
| modelStatus | modelPath                                                                                                                   |
+-------------+-----------------------------------------------------------------------------------------------------------------------------+
| saved       | http://db4ai-collie-cn-hangzhou.oss-cn-hangzhou.aliyuncs.com/xxxxx.pkl?OSSAccessKeyId=xxxxxx&Expires=xxxx&Signature=xxxxxx  |
+-------------+-----------------------------------------------------------------------------------------------------------------------------+
1 row in set (0.23 sec)

Note

The modelPath URL is valid for 100 minutes. The Expires parameter in the modelPath URL shows the expiration time. Access the URL before it expires.

View model details

Syntax

DESCRIBE MODEL model_name

Parameters

Parameter	Description
model_name	The model name.

Example

View the details of the airlines_gbm model.

/*polar4ai*/DESCRIBE MODEL airlines_gbm;

The following result is returned:

+-------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| model_name        | model_description                                                                                                                                                                                                                                                                 |
+-------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| airlines_gbm      | basic information:model_name:airlines_gbm model_class:lightgbm feature important:features,imp_gain,imp_splitAirline,0.3327,0.0376 AirportFrom,0.2178,0.1842 Time,0.1893,0.1999 AirportTo,0.1668,0.187 DayOfWeek,0.0384,0.1236 Length,0.0307,0.1269 Flight,0.0242,0.1408           |
+-------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
1 row in set (0.65 sec)

Evaluate a model

Evaluate the performance of a created model.

Syntax

SELECT select_expr [, select_expr] ... FROM EVALUATE (MODEL model_name, SELECT select_expr_for_prediction [, select_expr_for_prediction] ... FROM table_reference ) WITH (x_cols = '', y_cols='', metrics='')

Parameters

Parameter	Description
select_expr	The columns to display in the model evaluation results.
model_name	The model name.
select_expr_for_prediction	The columns to use for model evaluation.
table_reference	The table name.
x_cols	The input columns for model creation. Separate multiple column names with commas (,).
y_cols	You can create a model output column.
metrics	The model evaluation metric. Valid values: acc: Accuracy. This metric applies to classification tasks. r2_score: Coefficient of determination. This metric applies to regression tasks. ks: KS value. This value indicates the model's ability to distinguish between positive and negative samples. This metric applies to classification tasks. auc: AUC value. This value represents the area under the receiver operating characteristic (ROC) curve. This metric applies to classification tasks. Fscore: F-score. This is the harmonic mean of precision and recall rate. This metric applies to classification tasks.

Example

Use the created airlines_gbm model to perform model evaluation.

/*polar4ai*/SELECT Delay FROM evaluate(MODEL airlines_gbm, SELECT * FROM db4ai.airlines_test) WITH (x_cols = 'Airline,Flight,AirportFrom,AirportTo,DayOfWeek,Time,Length', y_cols='Delay', metrics='acc');

The following result is returned:

+--------------------------------------+
| task_id                              |
+--------------------------------------+
| df05244e-21f7-11ed-be66-xxxxxxxxxxxx |
+--------------------------------------+
1 row in set (0.95 sec)

Run the following command to check the task status.

/*polar4ai*/SHOW TASK `df05244e-21f7-11ed-be66-xxxxxxxxxxxx`;

The following result is returned:

+------------+----------------------------------------------------------------------------------------------------------------------------------------------------+-----------------+----------------------------+----------------------------+
| taskStatus | filePath                                                                                                                                           | results         | startTime                  | endTime                    |
+------------+----------------------------------------------------------------------------------------------------------------------------------------------------+-----------------+----------------------------+----------------------------+
| finish     | http://db4ai-collie-cn-hangzhou.oss-cn-hangzhou.aliyuncs.com/pc-xxxxxxx/airlines_gbm/xxxxx.csv?OSSAccessKeyId=xxxxxx&Expires=xxxx&Signature=xxxxxx | {"acc": 0.6694} | 2022-08-22 17:22:21.122101 | 2022-08-22 17:22:39.428811 |
+------------+----------------------------------------------------------------------------------------------------------------------------------------------------+-----------------+----------------------------+----------------------------+
1 row in set (0.24 sec)

Note

The filePath URL is valid for 100 minutes. The Expires parameter in the filePath URL shows the expiration time. Access the URL before it expires.

Perform model inference

Use a created model to perform online or offline inference.

Syntax

SELECT select_expr [, select_expr] ... FROM PREDICT (MODEL model_name, SELECT select_expr_for_prediction [, select_expr_for_prediction] ... FROM table_reference LIMIT row_count) WITH (x_cols= '')

Parameters

Parameter	Description
select_expr	The columns to display in the model inference results.
model_name	The model name.
select_expr_for_prediction	The columns to use for model inference.
table_reference	The table name.
mode	The mode. Set this parameter to async for offline inference. If you leave it empty, online inference is performed.
row_count	The number of samples for inference.
x_cols	The input columns for model creation. Separate multiple column names with commas (,).

Example

Online inference.

Use the created airlines_gbm model to perform online inference.

/*polar4ai*/SELECT Delay FROM PREDICT(MODEL airlines_gbm, SELECT * FROM db4ai.airlines_test LIMIT 10) WITH (x_cols = 'Airline,Flight,AirportFrom,AirportTo,DayOfWeek,Time,Length', y_cols='Delay');

The following result is returned:

+-------+-------------------+
| Delay | predicted_results |
+-------+-------------------+
|     1 | 0                 |
|     0 | 0                 |
|     0 | 0                 |
|     0 | 0                 |
|     0 | 0                 |
|     0 | 0                 |
|     1 | 0                 |
|     0 | 0                 |
|     0 | 0                 |
|     1 | 0                 |
+-------+-------------------+
10 rows in set (0.74 sec)

Offline inference.

If the number of inference samples exceeds 1,000, PolarDB for AI prompts you to use an offline task:

ERROR 9050 (HY000): Please limit the SQL selected data length to less than '1000' or convert to offline prediction

Create an offline task as follows:

/*polar4ai*/SELECT Delay FROM predict(MODEL airlines_gbm, SELECT * FROM db4ai.airlines_test) WITH (x_cols = 'Airline,Flight,AirportFrom,AirportTo,DayOfWeek,Time,Length', y_cols='Delay', mode='async');

The following result is returned:

+--------------------------------------+
| task_id                              |
+--------------------------------------+
| bd0c1722-21e7-11ed-94a8-xxxxxxxxxxxx |
+--------------------------------------+
1 row in set (0.75 sec)

The query returns the task_id of the task. You can use this task ID to query the task status and the download URL for the results.

/*polar4ai*/SHOW TASK `bd0c1722-21e7-11ed-94a8-xxxxxxxxxxxx`

The following result is returned:

+------------+----------------------------------------------------------------------------------------------------------------------------------------------------+-----------------+----------------------------+----------------------------+
| taskStatus | filePath                                                                                                                                           | results         | startTime                  | endTime                    |
+------------+----------------------------------------------------------------------------------------------------------------------------------------------------+-----------------+----------------------------+----------------------------+
| finish     | http://db4ai-collie-cn-hangzhou.oss-cn-hangzhou.aliyuncs.com/pc-xxxxxxx/airlines_gbm/xxxxx.csv?OSSAccessKeyId=xxxxxx&Expires=xxxx&Signature=xxxxxx |                 | 2022-08-22 14:57:51.355240 | 2022-08-22 14:58:18.316818 |
+------------+----------------------------------------------------------------------------------------------------------------------------------------------------+-----------------+----------------------------+----------------------------+
1 row in set (0.24 sec)

Note

The filePath URL is valid for 100 minutes. The Expires parameter in the filePath URL shows the expiration time. Access the URL before it expires.

Offline evaluation and inference tasks

Note

An offline task is a long-running batch processing job that does not return results in real time. Offline tasks can be used for evaluation or inference.

View the offline task list

View all offline tasks in the current cluster.

Syntax

SHOW TASKS

Example

Run the following command to view all offline tasks in the current cluster.

/*polar4ai*/SHOW TASKS;

The following result is returned:

+--------------------------------------+------------+-------------+----------------------------+----------------------------+
| task_id                              | task_type  | task_status | start_timestr              | end_timestr                |
+--------------------------------------+------------+-------------+----------------------------+----------------------------+
| 2cba0c74-1f8f-11ed-934a-xxxxxxxxxxxx | prediction | finish      | 2022-08-19 15:18:51.206829 |                            |
| 77b3a186-1f94-11ed-8eaa-xxxxxxxxxxxx | evaluation | finish      | 2022-08-19 15:56:44.465594 |                            |
| 972547a4-1fa3-11ed-9c6b-xxxxxxxxxxxx | evaluation | finish      | 2022-08-19 17:44:59.790353 | 2022-08-19 17:45:23.750100 |
| bd0c1722-21e7-11ed-94a8-xxxxxxxxxxxx | prediction | finish      | 2022-08-22 14:57:51.355240 | 2022-08-22 14:58:18.316818 |
| df05244e-21f7-11ed-be66-xxxxxxxxxxxx | evaluation | finish      | 2022-08-22 16:53:20.381577 | 2022-08-22 16:53:37.189953 |
| ec956db8-21fb-11ed-8400-xxxxxxxxxxxx | evaluation | finish      | 2022-08-22 17:22:21.122101 | 2022-08-22 17:22:39.428811 |
+--------------------------------------+------------+-------------+----------------------------+----------------------------+
9 rows in set (0.18 sec)

View offline task status

View the current status of an offline task. The task can have the following statuses:

init: The task is being initialized.
running: The task is running.
finish: The task is complete.
fail: The task failed.

Syntax

DROP TASK `task_id`

Parameters

Parameter	Description
task_id	The task ID.

Example

Delete the job with the job ID df05244e-21f7-11ed-be66-xxxxxxxxxxxx.

/*Polar for AI*/DROP TASK `df05244e-21f7-11ed-be66-xxxxxxxxxxxx`

The following result is returned:

+------------+----------------------------------------------------------------------------------------------------------------------------------------------------+-----------------+----------------------------+----------------------------+
| taskStatus | filePath                                                                                                                                           | results         | startTime                  | endTime                    |
+------------+----------------------------------------------------------------------------------------------------------------------------------------------------+-----------------+----------------------------+----------------------------+
| finish     | http://db4ai-collie-cn-hangzhou.oss-cn-hangzhou.aliyuncs.com/pc-xxxxxxx/airlines_gbm/xxxxx.csv?OSSAccessKeyId=xxxxxx&Expires=xxxx&Signature=xxxxxx | {"acc": 0.6694} | 2022-08-22 17:22:21.122101 | 2022-08-22 17:22:39.428811 |
+------------+----------------------------------------------------------------------------------------------------------------------------------------------------+-----------------+----------------------------+----------------------------+
1 row in set (0.24 sec)

Note

The filePath URL is valid for 100 minutes. The Expires parameter in the filePath URL shows the expiration time. Access the URL before it expires.

Delete an offline task

Syntax

DROP TASK `task_id`

Parameters

Parameter	Description
task_id	The ID of the task.

Example

Delete the task with the ID df05244e-21f7-11ed-be66-xxxxxxxxxxxx.

/*polar4ai*/DROP TASK `df05244e-21f7-11ed-be66-xxxxxxxxxxxx`

Delete a model

When a model is no longer needed, use this syntax to delete it.

Syntax

DROP MODEL model_name

Parameters

Parameter	Description
model_name	The model name.

Example

Delete the airlines_gbm model.

/*polar4ai*/DROP MODEL airlines_gbm

A result similar to the following is returned, which indicates that the airlines_gbm model is deleted:

Query OK, 0 rows affected (0.57 sec)

Best practices for custom models

This section provides an example of the end-to-end workflow for managing a custom model. This best practice covers offline model creation and online inference.

Create a model offline.

The following script shows how to create a model offline using the LightGBM algorithm:

# coding: utf-8
from pathlib import Path

import pandas as pd
from sklearn.metrics import mean_squared_error

import lightgbm as lgb
import joblib

def train_model():
    print('Loading data...')
    # load or create your dataset
    df_train = pd.read_csv('regression.train', header=None, sep='\t')
    df_test = pd.read_csv('regression.test', header=None, sep='\t')

    y_train = df_train[0]
    y_test = df_test[0]
    X_train = df_train.drop(0, axis=1)
    X_test = df_test.drop(0, axis=1)

    # create dataset for lightgbm
    lgb_train = lgb.Dataset(X_train, y_train)
    lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train)


    # specify your configurations as a dict
    params = {
        'boosting_type': 'gbdt',
        'objective': 'regression',
        'metric': {'l2', 'l1'},
        'num_leaves': 31,
        'learning_rate': 0.05,
        'feature_fraction': 0.9,
        'bagging_fraction': 0.8,
        'bagging_freq': 5,
        'verbose': 0
    }

    print('Starting training...')
    # train
    gbm = lgb.train(params,
                    lgb_train,
                    num_boost_round=20,
                    valid_sets=lgb_eval,
                    callbacks=[lgb.early_stopping(stopping_rounds=5)])


    print('Saving model...')
    # save model to file
    # gbm.save_model('model.txt')
    joblib.dump(gbm, 'lgb.pkl')

    print('Starting predicting...')
    # predict
    y_pred = gbm.predict(X_test, num_iteration=gbm.best_iteration)
    # eval
    rmse_test = mean_squared_error(y_test, y_pred) ** 0.5
    print(f'The RMSE of prediction is: {rmse_test}')

The model file is exported in .pkl format. The predict method is called to return the inference result. You must also provide a Python dependency file for the model to run.

The following is an example of the requirements.txt file:

lightgbm==3.3.3

Upload the model.

Run the following command to upload the model to PolarDB for AI.
```
/*polar4ai*/UPLOAD MODEL my_model WITH (model_location='https://xxxx.oss-cn-hangzhou.aliyuncs.com/xxxx/model.pkl?Expires=xxxx&OSSAccessKeyId=xxxx&Signature=xxxx', req_location='https://xxxx.oss-cn-hangzhou.aliyuncs.com/xxxx/requirements.txt?Expires=xxxx&OSSAccessKeyId=xxxx&Signature=xxxx')
```
model_location is the URL of the model file, and req_location is the URL of the file that contains the runtime dependencies. Prepare these two files, upload them to your private OSS bucket, and then use the command to upload them to the PolarDB for AI platform.
The following result is returned:
```
Query OK, 0 rows affected (0.29 sec)
```

Run the following command to check the model status.

/*polar4ai*/ SHOW MODEL my_model;

The following result is returned:

+-------------+-----------------------------------------------------------------------------------------------------------------------------+
| modelStatus | modelPath                                                                                                                   |
+-------------+-----------------------------------------------------------------------------------------------------------------------------+
| saved       | http://db4ai-collie-cn-hangzhou.oss-cn-hangzhou.aliyuncs.com/xxxxx.pkl?OSSAccessKeyId=xxxxxx&Expires=xxxx&Signature=xxxxxx  |
+-------------+-----------------------------------------------------------------------------------------------------------------------------+
1 row in set (0.23 sec)

If modelStatus is saved, the model is uploaded.

Deploy the model.

Run the following command to deploy the model on PolarDB for AI.
```
/*polar4ai*/ DEPLOY MODEL my_model;
```
The following result is returned:
```
Query OK, 0 rows affected (0.29 sec)
```

Run the following command to check the model status.

/*polar4ai*/ SHOW MODEL my_model;

The following information is returned:

+-------------+-----------------------------------------------------------------------------------------------------------------------------+
| modelStatus | modelPath                                                                                                                   |
+-------------+-----------------------------------------------------------------------------------------------------------------------------+
| serving     | http://db4ai-collie-cn-hangzhou.oss-cn-hangzhou.aliyuncs.com/xxxxx.pkl?OSSAccessKeyId=xxxxxx&Expires=xxxx&Signature=xxxxxx  |
+-------------+-----------------------------------------------------------------------------------------------------------------------------+
1 row in set (0.23 sec)

A modelStatus of serving indicates that the model is deployed successfully.

Perform online inference.

Run the following command to perform an online model inference task.

/*polar4ai*/ SELECT Y FROM PREDICT(MODEL my_model, SELECT * FROM db4ai.regression_test LIMIT 10) WITH (x_cols = 'x1,x2,x3,x4,x5,x6,x7,x8,x9,x10,x11,x12,x13,x14,x15,x16,x17,x18,x19,x20,x21,x22,x23,x24,x25,x26,x27,x28', y_cols='');

The following result is returned:

+------+---------------------+
| Y    | predicted_results   |
+------+---------------------+
|  1.0 | 0.6262147669037363  |
|  0.0 | 0.5082804008241021  |
|  0.0 | 0.37533158372209957 |
|  1.0 | 0.461974928099089   |
|  0.0 | 0.3777339456553666  |
|  0.0 | 0.35045096227525735 |
|  0.0 | 0.4178165504012342  |
|  1.0 | 0.40869795422774036 |
|  1.0 | 0.6826481286570045  |
|  0.0 | 0.47021259543154736 |
+------+---------------------+
10 rows in set (0.95 sec)

Alibaba Cloud is responsible for the maintenance of the underlying infrastructure and the software provided by Alibaba Cloud, such as the service's technical architecture and operating system. You are responsible for everything above the operating system, such as the applications you install. The instance's runtime environment belongs to you. If you upgrade the operating system yourself, you may experience adverse effects, such as downtime. Proceed with caution and be aware of the risks.