Tutorial | Model monitoring with a model evaluation store#

Get started#

Tutorial | Real-time API deployment demonstrated how to deploy an API service into production. However, after deploying, the Unified Monitoring screen showed an empty model status.

If a model will be used in production, we need to consider how to monitor it. As we get further in time from the training data, how do we ensure our model stays relevant? Let’s tackle those questions now.

Objectives#

In this tutorial, you will:

Use the Evaluate recipe and a model evaluation store (MES) to monitor model metrics in situations where you do and do not have access to ground truth data.
Conduct drift analysis to interpret how well the model is performing compared to its initial training.
Create a scenario to retrain the model based on a metric collected in the MES.
Create a model monitoring dashboard.

Prerequisites#

To reproduce the steps in this tutorial, you’ll need:

Dataiku 12.0 or later.
A Full Designer user profile on the Dataiku for AI/ML or Enterprise AI packages.
The Reverse Geocoding plugin (version 2.1 or above) installed on your Dataiku instance. (This plugin is installed by default on Dataiku Cloud).
Broad knowledge of Dataiku (ML Practitioner + Advanced Designer level or equivalent).

Create the project#

We’ll start from a project that includes a basic classification model and a zone for scoring new, incoming data.

From the Dataiku Design homepage, click +New Project > DSS tutorials > MLOps Practitioner > Model Monitoring Basics.
From the project homepage, click Go to Flow (or g + f).

Note

You can also download the starter project from this website and import it as a zip file.

Use case summary#

We’ll work with a simple credit card fraud use case. Using data about transactions, merchants, and cardholders, we have a Flow including a model that predicts which transactions should be authorized and which are potentially fraudulent.

A score of 1 for the target variable, authorized_flag, represents an authorized transaction.
A score of 0, on the other hand, is a transaction that failed authorization.

Putting this model into production can enable two different styles of use cases commonly found in machine learning workflows:

Scoring framework	Example use case	Production environment
Batch	A bank employee creates a monthly fraud report.	Automation node
Real-time	A bank’s internal systems authorize each transaction as it happens.	API node

Tip

This use case is just an example to practice monitoring and deploying MLOps projects into production. Rather than thinking about the data here, consider how you’d apply the same techniques and Dataiku features to solve problems that matter to you!

Review the Score recipe#

Before we use the Evaluate recipe for model monitoring, let’s review the purpose of the Score recipe.

Tip

If the Score recipe is unfamiliar to you, see the Scoring Basics Academy course in the ML Practitioner learning path.

The classification model found in the Flow was trained on three months of transaction data between January and March 2017. The new_transactions dataset currently holds the next month of transactions (April 2017).

Verify the contents of the new_transactions dataset found in the Model Scoring Flow zone by navigating to its Settings tab.
Click List Files to find /transactions-prepared-2017-04.csv as the only included file.

Note

The new_transaction_data folder feeding the new_transactions dataset holds nine CSV files: one for each month following the model’s training data. This monthly data has already been prepared using the same transformations as the model’s training data, and so it’s ready to be scored or evaluated.

It also is already labeled. In other words, it has known values for the target authorized_flag column. However, we can ignore these known values, for example, when it comes to scoring or input drift monitoring.

For this quick review, assume new_transactions has empty values for authorized_flag. If this were the case, our next step would be to input these new unknown records and the model to the Score recipe in order to output a prediction of how likely each record is to be fraudulent.

In the Model Scoring Flow zone, select the test_scored dataset.
In the Actions tab of the right panel, select Build.
Click Build Dataset with the Build Only This setting.

When finished, compare the schema of new_transactions and test_scored. The Score recipe adds three new columns (proba_0, proba_1, and prediction) to the test_scored dataset.

The Score recipe outputs predictions for new records, but how do we know if these predictions are similar to those produced during model training? That is the key question our monitoring setup will try to address.

Create two model monitoring pipelines#

There are two basics approaches to model monitoring, and we’ll need a separate pipeline for each one.

Ground truth vs. input drift monitoring#

Over time, a model’s input data may trend differently from its training data. Therefore, a key question for MLOps practitioners is whether a model is still performing well or if it has degraded after being deployed. In other words, is there model drift?

To definitively answer this question, we must know the ground truth, or the correct model output. However, in many cases, obtaining the ground truth can be slow, costly, or incomplete.

In such cases, we must instead rely on input drift evaluation. Using this approach, we compare the model’s training data against the new production data to see if there are significant differences.

A model evaluation store for ground truth monitoring#

Let’s start by creating the model monitoring pipeline for cases where the ground truth is available. For this, we’ll need the scored dataset.

From the Model Scoring Flow zone, select both the saved model and the test_scored dataset.
In the Actions tab of the right panel, select the Evaluate recipe.
For Outputs, Set an evaluation store named mes_for_ground_truth.
Click Create Evaluation Store, and then Create Recipe.
For the settings of the Evaluate recipe, adjust the sampling method to Random (approx. nb. records), and keep the default of 10,000.
Instead of running the recipe, click Save.

Take a moment to organize the Flow.

From the Flow, select the mes_for_ground_truth
In the Actions tab, select Move.
Select New Zone.
Name the new zone Ground Truth Monitoring.
Click Confirm.

Tip

See the following image below to check your work once you have both pipelines in place.

A model evaluation store for input drift monitoring#

Now let’s create a second model evaluation store for cases where the ground truth is not present following the same process. This time though, we’ll need the “new” transactions, which we can assume have an unknown target variable.

From the Model Scoring Flow zone, select both the saved model and the new_transactions dataset.
In the Actions tab of the right panel, select the Evaluate recipe.
For Outputs, Set an evaluation store named mes_for_input_drift.
Click Create Evaluation Store, and then Create Recipe.
As before, adjust the sampling method to Random (approx. nb. records), and keep the default of 10,000.

Because we are making the assumption that the input data is unlabeled, there is one important difference in the configuration of the Evaluate recipe for input drift monitoring.

In the the Output tile of the Evaluate recipe’s Settings tab, check the box Skip performance metrics computation.

Caution

If you do not have the ground truth, you won’t be able to compute performance metrics, and so the recipe would return an error without changing this setting.
Save the recipe, and return to the Flow.
Following the steps above, move the mes_for_input_drift into a new Flow zone called Input Drift Monitoring.

Review the two monitoring zones#

We now have one Flow zone dedicated to model monitoring using the ground truth and another Flow zone for the input drift approach. Both use the same model as input to the Evaluate recipe, but there are two important differences.

One difference is the input data to the Evaluate recipe:

The ground truth monitoring zone uses labeled, scored records.
The input drift monitoring zone uses unlabeled records (even though in this case, the actual data is labeled).

The second difference is that the Evaluate recipe in the input drift monitoring zone does not compute performance metrics (since we are assuming here that we do not have ground truth does).

Compare and contrast model monitoring pipelines#

These model evaluation stores are still empty! Let’s evaluate the April 2017 data, the first month beyond our model’s training data.

Build the MES for ground truth monitoring#

Let’s start with the model evaluation store that will have all performance metrics.

In the Ground Truth Monitoring Flow zone, select the mes_for_ground_truth.
In the Actions tab of the right panel, select Build.
Click Build Model Evaluation Store with the Build Only This setting.
When the job finishes, open the mes_for_ground_truth.
For the single model evaluation at the bottom, scroll to the right, and observe a full range of performance metrics.

Important

One run of the Evaluate recipe produces one model evaluation.

A model evaluation contains both metadata on the model and input, but also the computed metrics (in this case on data, prediction, and performance).

Build the MES for input drift monitoring#

Now let’s compare it the model evaluation store without performance metrics.

In the Input Drift Monitoring Flow zone, select the mes_for_input_drift.
In the Actions tab of the right panel, select Build.
Click Build Model Evaluation Store with the Build Only This setting.
When the job finishes, open the mes_for_input_drift.
In the model evaluation row, or the “Metrics to display” window, observe how performance metrics are not available.

Note

If you examine the job log for building either MES, you may notice an ML diagnostic warning — in particular, a dataset sanity check. As we’re not focused on the actual quality of the model, we can ignore this warning, but in a live situation, you’d want to play close attention to such warnings.

Run more model evaluations#

Before diving into the meaning of these metrics, let’s add more data to the pipelines for more comparisons between the model’s training data and the new “production” data found in the new_transaction_data folder.

Get a new month of transactions#

In the Model Scoring Flow zone, navigate to the Settings tab of the new_transactions dataset.
In the Files subtab, confirm that the Files selection field is set to Explicitly select files.
Click the trash can to remove /transactions_prepared_2017_04.csv.
On the right, click List Files to refresh.
Check the box to include /transactions_prepared_2017_05.csv.
Click Save.
Refresh the page to confirm that the dataset now only contains data from May.

Rebuild the MES for input drift monitoring#

We can immediately evaluate the new data in the Input Drift Monitoring Flow zone.

In the Input Drift Monitoring Flow zone, select the mes_for_input_drift.
In the Actions tab of the right panel, select Build.
Click Build Model Evaluation Store with the Build Only This setting.

Rebuild the MES for ground truth monitoring#

For ground truth monitoring, we first need to send the data through the Score recipe to maintain consistency.

In the Ground Truth Monitoring Flow zone, select the mes_for_ground_truth.
In the Actions tab of the right panel, select Build.
Select Build Upstream.
Click Preview to confirm that the job will run first the Score recipe and then the Evaluate recipe.
Click Run.
Open the mes_for_ground_truth to find a second model evaluation when the job has finished.

Tip

At this point, both model evaluation stores should have two rows (two model evaluations). Feel free to repeat the process above for the months of June and beyond so that your model evaluation stores have more data to compare.

Conduct drift analysis#

Now that we have some evaluation data to examine, let’s dive into what information the model evaluation store contains. Recall that our main concern is the model becoming obsolete over time.

The model evaluation store enables monitoring of three different types of model drift:

Input data drift
Prediction drift
Performance drift (when ground truth is available)

Input data drift#

Input data drift analyzes the distribution of features in the evaluated data.

Open the mes_for_ground_truth.
For the most recent model evaluation at the bottom, click Open.
Navigate to the Input data drift panel.
Explore the visualizations, clicking Compute as needed.

Note

See the reference documentation on Input Data Drift to understand how these figures can provide an early warning sign of model degradation.

Prediction drift#

Prediction drift analyzes the distribution of predictions on the evaluated data.

Remaining within the mes_for_ground_truth, navigate to the Prediction drift panel.
If not already present, click Compute, and explore the output in the fugacity and predicted probability density chart.

Performance drift#

Performance drift analyzes whether the actual performance of the model changes.

Lastly, navigate to the Performance drift panel of the mes_for_ground_truth.
If not already present, click Compute, and explore the table and charts comparing the performance metrics of the current test_scored dataset and reference training data.

Note

Thus far, we’ve only examined the drift analysis for the MES that computes performance metrics. Check the other MES to confirm that performance drift is not available. Moreover, you need to be using at least Dataiku 11.3 to have the prediction drift computed without ground truth.

Automate model monitoring#

Of course, we don’t want to manually build the model evaluation stores every time new data is available. We can automate this task with a scenario.

In addition to scheduling the computation of metrics, we can also automate actions based on the results. For example, assume our goal is to automatically retrain the model if a certain metric (such as data drift) exceeds a certain threshold. Let’s create the bare bones of a scenario to accomplish this kind of objective.

Note

In this case, we will be monitoring a MES metric. We can also monitor datasets with data quality rules.

Create a check on a MES metric#

Our first step is to choose a metric important to our use case. Since it’s one of the most common, let’s choose data drift.

From the Flow, open the mes_for_input_drift, and navigate to the Settings tab.
Go to the Status checks subtab.
Click Metric Value is in a Numeric Range.
Name the check Data Drift < 0.4.
Choose Data Drift as the metric to check.
Set the Soft maximum to 0.3 and the Maximum to 0.4.
Click Check to confirm it returns an error.
Click Save.

Now let’s add this check to the display of metrics for the MES.

Navigate to the Status tab.
Click X/Y Metrics.
Add both the data drift metric and the new check to the display.
Click Save once more.

Tip

Here we’ve deliberately chosen a data drift threshold to throw an error. Defining an acceptable level of data drift is dependent on your use case.

Design the scenario#

Just like any other check, we now can use this MES check to control the state of a scenario run.

From the Jobs menu in the top navigation bar, open the Scenarios page.
Click + New Scenario.
Name the scenario Retrain Model.
Click Create.

First, we need the scenario to build the MES.

Navigate to the Steps tab of the new scenario.
Click Add Step.
Select Build / Train.
Name the step Build MES.
Click Add Item > Evaluation store > mes_for_input_drift > Add Item.

Next, the scenario should run the check we’ve created on the MES.

Still in the Steps tab, click Add Step.
Select Verify rules or run checks.
Name the step Run MES checks.
Click Add Item > Evaluation store > mes_for_input_drift > Add Item.

Finally, we need to build the model, but only in cases where the checks fail.

Click Add Step.
Select Build / Train.
Name the step Build model.
Click Add Item > Model > Predict authorized_flag (binary) > Add Item.
Change the Run this step setting to If some prior step failed (that step being the Run checks step).
Check the box to Reset failure state.
Click Save when finished.

Tip

For this demonstration, we’ll trigger the scenario manually. In real life cases, we’d create a trigger and/or a reporter based on how often or under what conditions the scenario should run and who should be notified. Try these features out in Tutorial | Automation scenarios and Tutorial | Scenario reporters.

Run the scenario#

Let’s introduce another month of data to the pipeline, and then run the scenario.

Return to the new_transactions dataset in the Model Scoring Flow zone.
On the Settings tab, switch the data to the next month as done previously.
Return to the Retrain Model scenario.
Click Run to manually trigger the scenario.
On the Last Runs tab, observe its progression.
Assuming your MES check failed as intended, open the saved model in the Flow to see a new active version!

Note

This goal of this tutorial is to cover the foundations of model monitoring. But you can also think about how this specific scenario would fail to meet real-world requirements.

For one, it retrained the model on the original data!
Secondly, model monitoring is a production task, and so this kind of scenario should be moved to the Automation node.

Create additional model monitoring assets#

Once you have your model monitoring setup in place, you can start building informative assets on top of it to bring more users into the model monitoring arena.

Create a model monitoring dashboard#

Initially the visualizations inside the MES may be sufficient, but you may soon want to embed these metrics inside a dashboard to share results with collaborators in an easier way.

From the Dashboards page (g + p), open the project’s default dashboard.
Click Edit.
Click + New Tile to add the first tile.
In the dialog, choose Model evaluation report.
Select mes_for_input_drift as the source store, and click Add.
Drag the corners of the insight so it occupies the full width of the page.
In the tile settings, open the dropdown under Model evaluation report options to choose to display a summary or a specific aspect of the report.

Although we could add much more detail, let’s add just one more tile.

Click + New Tile.
Choose Scenario.
With the Last runs option selected, select Retrain Model as the source scenario, and click Add.
Drag the corner of the insight to increase the height.
In the tile settings, explore the different displays in Scenario last runs options.
Click Save, and then View to see the foundation of a model monitoring dashboard.

Note

When even more customization is required, you’ll likely want to explore building a custom webapp (which can also be embedded inside a native dashboard).

Optional: Create MES metrics datasets#

Dataiku allows for dedicated metrics datasets on objects like datasets, saved models, and managed folders. We can do the same for model evaluation stores. These datasets can be particularly useful for feeding into charts, dashboards, and webapps.

Open either MES, and navigate to the Status tab.
Click the gear icon, and select Create dataset from metrics data.
If not already present, move the MES metrics dataset to its respective Flow zone.

What’s next?#

Congratulations! You have achieved a great deal in this tutorial! Most importantly, you:

Created pipelines to monitor a model in situations where you do and do not have access to ground truth data.
Used input drift, prediction drift, and performance drift to evaluate model degradation.
Designed a scenario to automate periodic model retraining based on the value of a MES metric.
Gave stakeholders visibility into this process with a basic dashboard.

Now we have pipelines to monitor the model, but they remain only on the Design node. For a real use case, we’d need to deploy this project into production using either a batch or real-time API framework.

Once we have this kind of project in production, see Tutorial | API endpoint monitoring to set up a monitoring system that centralizes the logs from the API node and monitors the responses of endpoints.

Tip

Although not discussed here, this project also includes data quality rules on the transactions_joined_prepared dataset (type g + q to see them). In addition to model monitoring, data monitoring is a key aspect of an MLOps strategy!