Model Monitoring Example

The degradation of production models is a normal and expected event that can happen for several reasons. Some of the reasons are:

Data drift: This occurs when there is a change in the profile of the data based on which predictions are being made.
Concept drift: This happens when the expectations of what constitutes correct predictions change over time. For example, the statistical properties of a target variable, such as stocking inventory, may change over time as consumer preferences change.

The large variety of factors that contribute to data and concept drift make model monitoring an important task. It enables users to be aware when these changes compromise the prediction quality of production models.

OML Services model monitoring is supported for classification and regression models, and performance is tracked using model accuracy metrics. For classification, the model quality metrics include:

Accuracy: Calculates the proportion of correctly classifies cases - both Positive and Negative. For example, if there are a total of TP (True Positives)+TN (True Negatives) correctly classified cases out of TP+TN+FP+FN (True Positives+True Negatives+False Positives+False Negatives) cases, then the formula is: Accuracy = (TP+TN)/(TP+TN+FP+FN)
Balanced Accuracy: Evaluates how good a binary classifier is. It is especially useful when the classes are imbalanced, that is, when one of the two classes appears a lot more often than the other. This often happens in many settings such as Anomaly Detection etc.
Recall: Calculates the proportion of actual Positives that is correctly classified.
Precision: Calculates the proportion of predicted Positives that is True Positive.
F1 Score: Combines precision and recall into a single number. F1-score is computed using harmonic mean which is calculated by the formula: F1-score = 2*(precision*recall)/(precision+recall)
AUC (Area under the ROC Curve): Provides an aggregate measure of discrimination regardless of the decision threshold. AUC - ROC curve is a performance measurement for the classification problems at various threshold settings.

For regression, the model accuracy metrics include:

R2: A statistical measure that calculates how close the data are to the fitted regression line. In general, the higher the value of R-squared, the better the model fits your data. The value of R2 is always between 0 to 1, where:
- 0 indicates that the model explains none of the variability of the response data around its mean.
- 1 indicates that the model explains all the variability of the response data around its mean.
Mean Squared Error: This is the mean of the squared difference of predicted and true targets.
Mean Absolute Error: This is the mean of the absolute difference of predicted and true targets.
Median Absolute Error: This is the median of the absolute difference between predicted and true targets.

Model Monitoring Workflow

To monitor machine learning models through OML REST Services, follow these steps:

Deploy a model through AutoML UI
Obtain the access token
Get the Model ID of the model to be used for monitoring
Create a model monitoring job
View the details of a model monitoring job
Update a model monitoring job (optional)
Enable a model monitoring job
View and understand the output of a model monitoring job

1. Deploy a Model

Before monitoring any machine learning model, you must deploy the model. For this, go to AutoML UI and:

Create an AutoML experiment if you opt for the automated way to build machine learning models, and Deploy a model

2: Obtain the Access Token

You must obtain an authentication token by using your Oracle Machine Learning (OML) account credentials to send requests to OML Services. To authenticate and obtain a token, use cURL with the -d option to pass the credentials for your Oracle Machine Learning account against the Oracle Machine Learning user management cloud service REST endpoint /oauth2/v1/token. Run the following command to obtain the access token:

$ curl -X POST --header 'Content-Type: application/json' --header 'Accept: application/json' -d '{"grant_type":"password", "username":"'<yourusername>'", 
"password":"' <yourpassword>'"}'"<oml-cloud-service-location-url>/omlusers/api/oauth2/v1/token"

Here,

-X POST specifies to use a POST request when communicating with the HTTP server.
-header defines the headers required for the request (application/json).
-d sends the username and password authentication credentials as data in a POST request to the HTTP server.
Content-Type defines the response format (JSON).
Accept defines the response format (JSON).
yourusername is the user name of a Oracle Machine Learning user with the default OML_DEVELOPER role.
yourpassword is the password for the user name.
oml-cloud-service-location-url is a URL containing the REST server portion of the Oracle Machine Learning User Management Cloud Service instance URL that includes the tenancy ID and database name. You can obtain the URL from the Development tab in the Service Console of your Oracle Autonomous AI Database instance.

3: Get the Model ID of the Model to be Used for Monitoring

To get the modelId , send a GET request to the deployment endpoint and specify the model URI.

Get Request to obtain the modelId:

$ curl -X GET "<oml-cloud-service-location-url>/omlmod/v1/deployment/HousePowerNN" \
       --header "Authorization: Bearer ${token}" | jq '.modelId'

In this example, the model URI is HousePowerNN

Note:

The model URI is provided by the user when deploying the model using the AutoML UI or when deploying the model through a REST client.

The GET request returns the following:

"modelId": "0bf13d1f-86a6-465d-93d1-8985afd1bbdb"

4: Create a Model Monitoring Job

After obtaining the access token, you can create a model monitoring job by sending a POST request to the deployment endpoint and by specifying the model URI. To create a model monitoring job, you require the model IDs for the models that you want to monitor. The request body may include a single model, or a list of up to 20 models identified by their model IDs.

Example of a POST Request for Model Monitoring Job Creation

This is an example of a POST request to create a model monitoring job. The job is submitted asynchronously. Therefore, it will run as scheduled and the results can be retrieved when the job completes.

In the jobSchedule parameter, specify the job start date, job end date, job frequency, and maximum number of runs.
In the jobProperty parameter, specify the model monitoring details such as:
- Model monitoring job name and job type
- Autonomous AI Database service level
- Table where the model monitoring details will be saved
- Drift alert trigger
- Threshold
- Maximum number of runs
- Baseline and new data to be used
- Chosen balanced accuracy for the performance metric
- Start date (optional ) and end date (optional ) correspond to the DATE or TIMESTAMP column in the table or view denoted by newData, and contained in the timeColumn field. If the start and end dates are not specified, the earliest and latest dates and times in the timeColumn are used.

$ curl -X POST "<oml-cloud-service-location-url>/omlmod/v1/jobs" \
         --header "Authorization: Bearer ${token}" \
         --header 'Content-Type: application/json' \
         --data '{
            "jobSchedule": {
            "jobStartDate": "2023-03-25T00:30:07Z",                 # job start date and time 
            "repeatInterval": "FREQ=DAILY",                         # job frequency
            "jobEndDate": "2023-03-30T20:50:06Z",                   # job end date and time 
            "maxRuns": "5"                                          # max runs within the schedule
         },
         "jobProperties": {
            "jobName": "MY_MODEL_MONITOR1",                         # job name
            "jobType": "MODEL_MONITORING",                          # job type; MODEL_SCORING
            "disableJob": false,                                    # flag to disable the job at submission
            "jobServiceLevel": "LOW",                               # Autonomous AI Database service level; either LOW, MEDIUM, and HIGH
            "inputSchemaName": "OMLUSER",                           # database schema that owns the input table/view
            "outputSchemaName": "OMLUSER",                          # database schema that owns the output table
            "outputData": "Global_Active_Power_Monitor",            # table where the job results will be saved in the format {jobID}_{outputData}
            "jobDescription": "Global active power monitoring job", # job description
            "baselineData": "HOUSEHOLD_POWER_BASE",                 # table/view containing baseline data 
            "newData": "HOUSEHOLD_POWER_NEW",                       # table/view with new data to compare against baseline
            "frequency": "Year",                                    # time window unit that the monitoring is done on in the new data
            "threshold": 0.15,                                      # threshold to trigger drift alert
            "timeColumn": "DATES",                                  # date or timestamp column in newData
            "startDate": "2008-01-01T00:00:00Z",                    # the start date and time of monitoring in the new data
            "endDate": "2010-11-26T00:00:00Z",                      # the end date and time of monitoring in the new data
            "caseidColumn": null,                                   # case identifier column in the baseline and new data
            "performanceMetric": "MEAN_SQUARED_ERROR",              # metric used to measure model performance
            "modelList": [                                          # model ID or list of model IDs to be monitored
                "0bf13d1f-86a6-465d-93d1-8985afd1bbdb"
             ],
            "recompute": false                                      # flag to determine whether to overwrite the results table
         }
    }' | jq

The parameters to run this job are categorized into:

Required parameters:

jobType: Specifies the type of job to be run, and is set to MODEL_MONITORING for model monitoring jobs
outputData: The output data identifier. The results of the job is written to a table named {jobId}_{ouputData}
baselineData: The table or view that contains baseline data to monitor. At least 50 rows per period are required for model monitoring, otherwise the analysis is skipped
newData: The table or view with new data to be compared against the baseline. At least 50 rows per period are required for model monitoring, otherwise the analysis is skipped
modelList: The list of models to be monitored, identified by their modelIds. By default, up to 20 models can be monitored by a single job

Optional Parameters:

disableJob: A flag to disable the job at submission. If not set, the default is false and the job is enabled at submission.
timeColumn: The column name containing date or the timestamp column in the new data. If not provided, the entire newData is treated as one period.
frequency: Indicates the unit of time for which the monitoring is done on with the new data. The frequency can be "day", "week", "month", or "year". If not provided, the entire "new" data is used as a single time period.
threshold: The threshold to trigger a drift alert.
recompute: A flag on whether to update the already computed periods. The default is False. This means that only time periods not present in the output result table will be computed.
performanceMetric: The metric used to measure model performance.

Note:
For regression models, the default is MEAN_SQUARED_ERROR. For classification models, the default is BALANCED_ACCURACY.
caseidColumn: A case identifier column in the baseline and new data. Providing it improves the reproducibility of results.
startDate: The start date or timestamp of monitoring in the newData column. The column timeColumn is mandatory for startDate. If startDate is not provided, then startDate depends on whether frequency is provided. If frequency is not provided, then the earliest date in timeColumn is used as the startDate. If both startDate and frequency are not provided, then the most recent of the earliest date in timeColumn and the starting date of the 10^th most recent cycle is considered as the startDate.

Note:
The supported date and time format is the ISO-8601 date and time format. For example: 2022-05-12T02:33:16Z
endDate: The end date or timestamp of monitoring in the newData. The column timeColumn is mandatory for endDate. If endDate is not provided, then the most recent date in timeColumn will be used.

Note:
The supported date and time format is the ISO-8601 date and time format. For example: 2022-05-12T02:33:16Z
jobDescription: A text description of the job.
outputSchemaName: The database schema that owns the output table. If not specified, the output schema will be the same as the input schema.
inputSchemaName: The database schema that owns the input table or view. If not specified, the input schema will be the same as the username in the request token.
jobServiceLevel: The service level for the job, which can be LOW, MEDIUM, or HIGH.

Response of the POST Request for Job Creation

Here is an example of a model monitoring job creation response:

{
  "jobId": "OML$736F509B_FC1A_400A_AC75_553F1D6C5D97",
  "links": [
    {
      "rel": "self",
      "href": "<OML Service URL>/v1/jobs/OML%24736F509B_FC1A_400A_AC75_553F1D6C5D97"
    }
  ]
}

When the job is successfully submitted, you will receive a response with the job ID. Note the jobId for future reference to submit requests for retrieving job details or to perform any action on the job.

5: View Details of the Submitted Job

To view the details of your submitted job, send a GET request to the /omlmod/v1/jobs/{jobId} endpoint, where jobId is the ID provided in response to the successful submission of your model monitoring job.

Example of a GET request to view details of a submitted job

$ export jobid='OML$736F509B_FC1A_400A_AC75_553F1D6C5D97'   # define the Job ID as a single-quoted variable 

$ curl -X GET "<oml-cloud-service-location-url>/omlmod/v1/jobs/${jobid}"  \
       --header 'Accept: application/json' \
       --header 'Content-Type: application/json' \
       --header "Authorization: Bearer ${token}" | jq

Here is a sample output of the job details request. The jobStatus CREATED indicates that the job has been created. If your job has already run once, you will see information returned about the last job run.

Response of the GET request

returns:

{
  "jobId": "OML$736F509B_FC1A_400A_AC75_553F1D6C5D97",
  "jobRequest": {
    "jobSchedule": {
      "jobStartDate": "2023-03-25T00:30:07Z",
      "repeatInterval": "FREQ=DAILY",
      "jobEndDate": "2023-03-30T00:30:07Z",
      "maxRuns": 5
    },
    "jobProperties": {
      "jobType": "MODEL_MONITORING",
      "inputSchemaName": "OMLUSER",
      "outputSchemaName": "OMLUSER",
      "outputData": "Global_Active_Power_Monitor",
      "jobDescription": "Global active power monitoring job",
      "jobName": "MY_MODEL_MONITOR1",
      "disableJob": false,
      "jobServiceLevel": "LOW",
      "baselineData": "HOUSEHOLD_POWER_BASE",
      "newData": "HOUSEHOLD_POWER_NEW",
      "timeColumn": "DATES",
      "startDate": "2008-01-01T00:00:00Z",
      "endDate": "2010-11-26T00:00:00Z",
      "frequency": "Year",
      "threshold": 0.15,
      "recompute": false,
      "caseidColumn": null,
      "modelList": [
        "0bf13d1f-86a6-465d-93d1-8985afd1bbdb"
      ],
      "performanceMetric": "MEAN_SQUARED_ERROR"
    }
  },
  "jobStatus": "CREATED",
  "dateSubmitted": "2023-03-25T00:26:16.127906Z",
  "links": [
    {
      "rel": "self",
      "href": "<OML Service URL>/omlmod/v1/jobs/OML%24736F509B_FC1A_400A_AC75_553F1D6C5D97"
    }
  ],
  "jobFlags": [],
  "state": "SCHEDULED",
  "enabled": true,
  "runCount": 0,
  "nextRunDate": "2023-03-25T00:30:07Z"
}

6: Update the Model Monitoring Job (Optional)

After an asynchronous job is submitted, you have the option to update the job. Send a POST request to the /omlmod/v1/jobs/{jobID} endpoint to update a job.

For this job, you must update the following parameters:

startDate
endDate
threshold
recompute
modelList

This is an example to update a model monitoring job. Here, the same job ID is used to update some of the original parameters in the updateProperties field. The trigger for the drift alert is updated to 0.20, and the flag recompute is set to update the already computed periods so that each job run will recalculate all time periods present in the specified timeColumn in the data.

$ curl -i -X POST "<oml-cloud-service-location-url>/omlmod/v1/jobs/${jobid}" \    
         --header "Authorization: Bearer ${token}" \
         --header 'Content-Type: application/json' \
         --data '{
            "updateProperties": {
                "threshold": 0.20,
                "recompute": "true"
            }
         }'

Note:

A successful update will return an HTTP 204 response with no content.

7: View the Model Monitoring Job Output

Once your job has run, either according to its schedule or by the RUN action, you can view its output in the table you specified in your job request with the outputData parameter. The full name of the table is {jobId}_{outputData}. You can check if your job is complete by sending a request to view its details. If your job has run at least once you should see the lastRunDetail parameter with information on that run.

%sql


SELECT IS_BASELINE, MODEL_ID, round(METRIC, 4), HAS_DRIFT, round(DRIFT, 4), MODEL_TYPE, 
       THRESHOLD, MODEL_METRICS 
FROM OML$736F509B_FC1A_400A_AC75_553F1D6C5D97_Global_Active_Power_Monitor

The command returns a table with the columns IS_BASELINE, MODEL_ID, ROUND (METRIC, 4), HAS_DRIFT, ROUND (DRIFT, 4), MODEL_TYPE, THRESHOLD, and MODEL_METRICS. Note that the first row of results is the baseline time period. As drift is not calculated on data in the baseline time period, that is why the columns HAS_DRIFT , ROUND (DRIFT, 4), and THRESHOLD are empty for this row.

6: Perform an Action on a Model Monitoring Job (Optional)

When your job has been successfully submitted, its state is set to ENABLED by default. This means that it will run as per the schedule you specified when submitting the job unless its updated to another state, such as DISABLED. You can do this by sending a request to the /omlmod/v1/jobs/{jobid}/action endpoint.

OML Services interacts with the DBMS_SCHEDULER to perform actions on jobs. There are four options for actions that can be sent to this endpoint:

DISABLE: Disables the job at submission. The force property can be used with this action to forcefully interrupt any running job.

Note:
Jobs can be set to DISABLED at submission by setting the disableJob flag to true.
ENABLE: Enables a job. After a disabled job is enabled, the scheduler begins to automatically run the job according to its schedule.
RUN: This option immediately runs the job as a way to test a job or run it outside of its schedule.
STOP: Stops a currently running job.

Here is an example of a POST request to update the job status to DISABLED.

$ curl -i -X POST "<oml-cloud-service-location-url>/omlmod/v1/jobs/${jobid}/action" \
       --header "Authorization: Bearer ${token}" \
       --header 'Content-Type: application/json' \
       --data '{
            "action": "DISABLE",
            "force": "false"
         }'

Note:

The force parameter is set to false by default. You can use it with the DISABLE action to interrupt a running job.

When you successfully submit your job you will receive a 204 response with no body.

7: Delete a Model Monitoring Job

To delete a previously submitted job, send a DELETE request along with the jobid to the /omlmod/v1/jobs endpoint.

Here is an example of a DELETE request to the /omlmod/v1/jobs endpoint:

$ curl -X DELETE "<oml-cloud-service-location-url>/omlmod/v1/jobs/${jobid}"  \
       --header 'Accept: application/json' \
       --header 'Content-Type: application/json' \
       --header "Authorization: Bearer ${token}" | jq

Recreate the Example (Optional)

The examples here uses the tables HOUSEHOLD_POWER_BASE and HOUSEHOLD_POWER_NEW. These tables are created using the Individual Household Electric Power Consumption data from the UCI Machine Learning Repository. To recreate the example described here, follow these steps:

Run this command in a R paragraph in an OML notebook to drop any tables, if it exists, and suppress warnings:
```
%r

options(warn=-1)

try(ore.drop(table="HOUSEHOLD_POWER_BASE"))
try(ore.drop(table="HOUSEHOLD_POWER_NEW"))
```

Run the following command in a R paragraph to read and transform data:

%r

test <- read.csv("https://objectstorage.us-sanjose-1.oraclecloud.com/n/adwc4pm/b/OML_Data/o/household_power_consumption.txt", sep=";")

test <- transform(test, Date = as.Date(Date, format = "%d/%m/%Y"))
test <- transform(test, Global_active_power = as.numeric(Global_active_power))
test <- transform(test, Global_reactive_power = as.numeric(Global_reactive_power))
test <- transform(test, Voltage = as.numeric(Voltage))
test <- transform(test, Global_intensity = as.numeric(Global_intensity))
test <- transform(test, Sub_metering_1 = as.numeric(Sub_metering_1))
test <- transform(test, Sub_metering_2 = as.numeric(Sub_metering_2))
test <- transform(test, Sub_metering_3 = as.numeric(Sub_metering_3))

colnames(test) <- c("DATES", "TIMES", "GLOBAL_ACTIVE_POWER", "GLOBAL_REACTIVE_POWER", "VOLTAGE", "GLOBAL_INTENSITY", "SUB_METERING_1", "SUB_METERING_2", "SUB_METERING_3")

Now create the baseline data HOUSEHOLD_POWER_BASE and new data HOUSEHOLD_POWER_NEW. For this, run this R script:

%r

test_base <- test[test$DATES < "2008-01-01",]
test_new <- test[test$DATES > "2007-12-31",]

# Create OML proxy objects

ore.create(test_base, table="HOUSEHOLD_POWER_BASE")
ore.create(test_new, table="HOUSEHOLD_POWER_NEW")

To view the baseline data HOUSEHOLD_POWER_BASE, run this SQL command in a SQL paragraph in the notebook:
```
%sql

SELECT * FROM HOUSEHOLD_POWER_BASE
FETCH FIRST 5 ROWS ONLY;
```

View the new data HOUSEHOLD_POWER_NEW by running this SQL command:

%sql

SELECT * FROM HOUSEHOLD_POWER_NEW
FETCH FIRST 5 ROWS ONLY;