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Drift Monitoring

Drift monitoring is the process of monitoring ML models for drift. As part of the MLOps process, drift monitoring ensures model performance and relevance.

What is Drift?

As the world changes, input data for ML models changes as well. As a result, models that were previously accurate become unstable and produce unreliable predictions. This is known as “drift”.

Model drift occurs when the data changes in relation to the baseline data set (for example, the training set) and produces inaccurate results. In other words, production data drifts and creates data integrity challenges.

In other cases, drift can occur due to data integrity issues. For example, when data pipelines malfunction and produce erroneous data.

Types of Drift

There are a few different types of drift. The main ones are:

  • Concept Drift – When the meaning of the input data has changed but the model in production does now know this and cannot make accurate predictions. This means the statistical properties of what the model is trying to predict (the target variable) change.
  • Data Drift / Virtual Drift – When the data underlying the model changed, but the model’s performance did not.
  • Feature Drift – When the model’s input data distribution changes.
  • Prediction Drift – When the model’s predictions shift.
  • Label Drift – When the model’s output or label distribution shifts.

What is Drift Monitoring?

Drift monitoring is the process of continuously tracking ML model’s performance in production. This ensures that new real-time data or data integrity did not degrade model quality. Drift monitoring includes ongoing analysis of the data, with techniques like sequential analysis, monitoring distribution between different time windows, adding timestamps to the decision tree based classifier, and more.

When drift is detected, a drift monitoring system will trigger alerts and update the existing models. This process takes place as part of the MLOps pipeline.

Why Do We Need to Monitor Drift?

Monitoring drift helps us detect drift to ensure our models will continue to perform and provide accurate predictions. By alerting about drift and retraining models to ensure their reliability, data scientists and engineers can ensure the models remain accurate, fair and unbiased. This is fundamental for the relevance of ML and for providing business value.

Automating Drift Monitoring

It is recommended to continuously monitor models to detect drift and ensure model stability. Monitoring can either be manual or automated. Automated drift monitoring is more accurate and saves data scientists time. If your use cases include streaming data, the monitoring system will also need to support automated real-time detection.

How to Monitor Drift

Drift monitoring takes place through a drift-aware system. Such a system will monitor data and determine how to manage new data and models. A drift-aware systems consists of four parts:

  • Change Detection – alerts about changes by using methods like SPC / Sequential Analysis Concept Drift detectors, monitoring distributions between different time windows and contextual approaches.
  • Memory – determines how new data will be used to train the model, through mechanisms like online learning, fixed window, variable window and gradually forgetting older data.
  • Learning – combines learning components to generalize from examples and update predictive models.
  • Loss estimation

Read more about drift-aware systems here.

Drift Monitoring with MLRun and Iguazio

Open source MLRun supports deployment and orchestration of production-ready AI applications. MLRun monitors models in production, and identifies and mitigates drift on the fly. Model drift detection is based on feature drift via the integrated feature store, and auto-triggers retraining. To see it in action, check out the MLRun Quickstart.