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Model monitoring overview

This document describes how BigQuery ML supports monitoring of machine learning (ML) models through evaluation and comparison the data a model uses. This includes comparing a model's serving data to its training data,and comparing new serving data to previously used serving data.

Understanding the data used by your models is a critical aspect of ML, because this data affects model performance. Understanding any variance between your training and serving data is especially important in ensuring that your models remain accurate over time. A model performs best on serving data that is similar to the training data. When the serving data deviates from the data used to train the model, the model's performance can deteriorate, even if the model itself hasn't changed.

BigQuery ML provides functions to help you analyze your training and serving data for data skew and data drift:

Data skew occurs when the distribution of feature values for training data is significantly different from serving data in production. Training statistics for the model are saved during model training, so the original training data isn't required for you to use skew detection.
Data drift occurs when feature data distribution in production changes significantly over time. Drift detection is supported for consecutive spans of data, for example, between different days of serving data. This lets you get notified if the serving data is changing over time, before the data sets diverge too much to retrain the model.

Use the following functions to monitor models in BigQuery ML:

ML.DESCRIBE_DATA: compute descriptive statistics for a set of training or serving data.
ML.VALIDATE_DATA_SKEW: compute the statistics for a set of serving data, and then compare them to the training data statistics that were computed when a BigQuery ML model was trained, in order to identify anomalous differences between the two data sets. Statistics are only computed for feature columns in the serving data that match feature columns in the training data, in order to achieve better performance and lower cost.
ML.VALIDATE_DATA_DRIFT: compute and compare the statistics for two sets of serving data in order to identify anomalous differences between the two data sets.
ML.TFDV_DESCRIBE: compute fine-grained descriptive statistics for a set of training or serving data. This function provides the same behavior as the TensorFlow tfdv.generate_statistics_from_csv API.
ML.TFDV_VALIDATE: compare the statistics for training and serving data statistics, or two sets of serving data statistics, in order to identify anomalous differences between the two data sets. This function provides the same behavior as the TensorFlow validate_statistics API.

Monitoring use cases

This section describes how to use the BigQuery ML model monitoring functions in common monitoring use cases.

Basic data skew monitoring

This use case is appropriate when you want to quickly develop and monitor a model for data skew and don't need fine-grained skew statistics to integrate with an existing monitoring solution.

Typical steps for this use case are as follows:

Run the ML.DESCRIBE_DATA function on your training and serving data, to make sure both data sets compare appropriately to each other and are within expected parameters.
Create a BigQuery ML model and train it on the training data.
Run the ML.VALIDATE_DATA_SKEW function to compare the serving data statistics with the training data statistics that were computed during model creation in order to see if there's any data skew.
If there is data skew, investigate the root cause, adjust the training data appropriately, and then retrain the model.

Basic data drift monitoring

This use case is appropriate when you want to quickly develop and monitor a model for data drift and don't need fine-grained drift statistics to integrate with an existing monitoring solution.

Typical steps for this use case are as follows:

Run the ML.DESCRIBE_DATA function on your training and serving data to make sure both data sets compare appropriately to each other and are within expected parameters.
Create a BigQuery ML model and train it on the training data.
Run the ML.VALIDATE_DATA_DRIFT function to compare the statistics for two different serving data sets in order to see if there's any data drift. For example, you might want to compare the current serving data to historical serving data from a table snapshot, or to the features served at a particular point in time, which you can get by using the ML.FEATURES_AT_TIME function.
If there is data drift, investigate the root cause, adjust the training data appropriately, and then retrain the model.

Advanced data skew or drift monitoring

This use case is appropriate when you want fine-grained skew or drift statistics to integrate with an existing monitoring solution or for other purposes.

Typical steps for this use case are as follows:

Run the ML.TFDV_DESCRIBE function on your training and serving data at intervals appropriate to your monitoring solution, and save the query results. This step lets you compare future serving data to training and serving data from past points in time.
Run the ML.TFDV_VALIDATE function on your training and serving data statistics, or on two sets of serving data statistics, to evaluate data skew or feature drift, respectively. The training and serving data must be provided as a TensorFlow DatasetFeatureStatisticsList protocol buffer in JSON format. You can generate a protocol buffer in the correct format by running the ML.TFDV_DESCRIBE function, or you can load it from outside of BigQuery. The following example shows how to evaluate feature skew:
```
DECLARE stats1 JSON;
DECLARE stats2 JSON;

SET stats1 = (
  SELECT * FROM ML.TFDV_DESCRIBE(TABLE `myproject.mydataset.training`)
);
SET stats2 = (
  SELECT * FROM ML.TFDV_DESCRIBE(TABLE `myproject.mydataset.serving`)
);

SELECT ML.TFDV_VALIDATE(stats1, stats2, 'SKEW');

INSERT `myproject.mydataset.serve_stats`
  (t, dataset_feature_statistics_list)
SELECT CURRENT_TIMESTAMP() AS t, stats1;
```
If there is data skew or data drift, investigate the root cause, adjust the training data appropriately, and then retrain the model.

Monitoring visualization

Some monitoring functions offer integration with Vertex AI model monitoring, so that you can use charts and graphs to analyze model monitoring function output.

Using Vertex AI visualizations offers the following benefits:

Interactive visualizations: explore data distributions, skew metrics, and drift metrics by using charts and graphs in the Vertex AI console.
Historical analysis: track model monitoring results over time by using Vertex AI visualizations. This lets you identify trends and patterns in data changes so that you can proactively update and maintain models.
Centralized management: manage monitoring for all BigQuery ML and Vertex AI models in the unified Vertex AI dashboard.

You can enable visualization of the ML.VALIDATE_DATA_DRIFT function output by using that function's MODEL argument. You can enable visualization of the ML.VALIDATE_DATA_SKEW function output by using that function's enable_visualization_link argument.

You can only use monitoring visualization with models that are registered with Vertex AI. You can register an existing model by using the ALTER MODEL statement.

Monitoring automation

You can automate monitoring by using a scheduled query to run the monitoring function, evaluate the output, and retrain the model if anomalies are detected. You must enable email notifications as part of setting up the scheduled query.

For an example that shows how to automate the ML.VALIDATE_DATA_SKEW function, see Automate skew detection.