Monitor data and model quality with Amazon SageMaker Model Monitor
Amazon SageMaker Model Monitor monitors the quality of Amazon SageMaker machine learning models in production. With Model Monitor, you can set up:
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Continuous monitoring with a real-time endpoint.
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Continuous monitoring with a batch transform job that runs regularly.
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On-schedule monitoring for asynchronous batch transform jobs.
With Model Monitor, you can set alerts that notify you when there are deviations in the model quality. Early and proactive detection of these deviations lets you to take corrective actions. You can take actions like retraining models, auditing upstream systems, or fixing quality issues without having to monitor models manually or build additional tooling. You can use Model Monitor prebuilt monitoring capabilities that do not require coding. You also have the flexibility to monitor models by coding to provide custom analysis.
Model Monitor provides the following types of monitoring:
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Monitor data quality - Monitor drift in data quality.
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Monitor model quality - Monitor drift in model quality metrics, such as accuracy.
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Monitor Bias Drift for Models in Production - Monitor bias in your model's predictions.
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Monitor Feature Attribution Drift for Models in Production - Monitor drift in feature attribution.
Topics
- Monitoring a Model in Production
- How Amazon SageMaker Model Monitor works
- Capture data
- Monitor data quality
- Monitor model quality
- Monitor Bias Drift for Models in Production
- Monitor Feature Attribution Drift for Models in Production
- Schedule monitoring jobs
- Amazon SageMaker Model Monitor prebuilt container
- Interpret results
- Visualize results for real-time endpoints in Amazon SageMaker Studio
- Advanced topics
- Model Monitor FAQs
How Amazon SageMaker Model Monitor works
Amazon SageMaker Model Monitor automatically monitors machine learning (ML) models in production and notifies you when quality issues happen. Model Monitor uses rules to detect drift in your models and alerts you when it happens. The following figure shows how this process works in the case that your model is deployed to a real-time endpoint.
You can also use Model Monitor to monitor a batch transform job instead of a real-time endpoint. In this case, instead of receiving requests to an endpoint and tracking the predictions, Model Monitor monitors inference inputs and outputs. The following figure diagrams the process of monitoring a batch transform job.
To enable model monitoring, take the following steps. These steps follow the path of the data through the various data collection, monitoring, and analysis processes.
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For a real-time endpoint, enable the endpoint to capture data from incoming requests to a trained ML model and the resulting model predictions.
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For a batch transform job, enable data capture of the batch transform inputs and outputs.
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Create a baseline from the dataset that was used to train the model. The baseline computes metrics and suggests constraints for the metrics. Real-time or batch predictions from your model are compared to the constraints. They are reported as violations if they are outside the constrained values.
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Create a monitoring schedule specifying what data to collect, how often to collect it, how to analyze it, and which reports to produce.
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Inspect the reports, which compare the latest data with the baseline. Watch for any violations reported, metrics, and notifications from Amazon CloudWatch.
Notes
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Model Monitor computes model metrics and statistics on tabular data only. For example, an image classification model that takes images as input and outputs a label based on that image can still be monitored. Model Monitor would be able to calculate metrics and statistics for the output, not the input.
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Model Monitor currently supports only endpoints that host a single model and does not support monitoring multi-model endpoints. For information on using multi-model endpoints, see Host multiple models in one container behind one endpoint.
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Model Monitor supports monitoring inference pipelines. However, capturing and analyzing data is done for the entire pipeline, not for individual containers in the pipeline.
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To prevent impact to inference requests, Data Capture stops capturing requests at high levels of disk usage. We recommended that you keep your disk utilization below 75% to ensure data capture continues capturing requests.
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If you launch SageMaker Studio in a custom Amazon VPC, you must create VPC endpoints to let Model Monitor communicate with Amazon S3 and CloudWatch. For information about VPC endpoints, see VPC endpoints in the Amazon Virtual Private Cloud User Guide. For information about launching SageMaker Studio in a custom VPC, see Connect Studio notebooks in a VPC to external resources.
Model Monitor sample notebooks
For a sample notebook that takes you through the end-to-end workflow using Model Monitor
with your real-time endpoint, see Introduction to Amazon SageMaker Model Monitor
For a sample notebook that visualizes the statistics.json file for a selected
execution in a monitoring schedule, see the Model Monitor Visualization
For instructions about how to create and access Jupyter notebook instances that you can use to run the example in SageMaker, see Amazon SageMaker Notebook Instances. After you have created a notebook instance and opened it, choose the SageMaker Examples tab to see a list of all the SageMaker samples. To open a notebook, choose the notebook's Use tab and choose Create copy.
