5 Tips For Model Monitoring To Ensure Data Quality

Model monitoring is an indispensable aspect of the Machine Learning (ML) workflow. It ensures that ML models continue to function appropriately and produce high-quality and accurate results to guide reliable decision making. This step is vital as it helps identify and address data quality problems or changes in the underlying data distribution(drift).

Poor data quality leads to poor model performance and unreliable predictions, and poor model performance can seriously affect industries that rely on data and Machine Learning such as healthcare, finance, transportation, manufacturing, and retail. In general, any industry that uses data-driven decision making can be impacted by poor model performance and unreliable predictions. In this article, we cover five tips for model monitoring to ensure data quality.

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Automated monitoring and alerting tools are designed to continuously track the performance of Machine Learning models and the data they use. These tools can identify and notify stakeholders of potential issues or abnormalities detected so timely corrective action can be taken to prevent model degradation and minimize the impact on users. Many of these tools also provide features for detecting and correcting biases in the data and ensuring that the data remains of high quality over time. This can be especially important in production environments, where the data’s quality and integrity can significantly impact the model’s performance and reliability. Some of these tools may be open-source or cloud-based and support many programming languages and tools.

Another important aspect of model monitoring is checking for data drift, which refers to changes in the underlying data distribution over time. This data drift occurs when the data that the ML model is applied to changes in a way that is not reflected in the training data and consequently leads to model performance decline. Different types of data drifts can occur:

• Concept Drift occurs when what you are trying to predict (the target variable) changes over time. A good example would be predicting the demand for a product based on historical sales data. The demand changes according to consumer preferences or the introduction of new competing products as time passes.
• Covariate Shift also happens when the input data used to make predictions changes over time, but the target variable stays the same. This can be observed when you try to predict whether or not a customer will dwindle and/or their preferences change (e.g., they start using your service in different ways).
• Prior Probability Shift occurs when the relative frequency of different target classes changes over time. This can happen when we try predicting whether or not a patient has a certain disease based on their symptoms, but the prevalence of the disease changes based on the advances during treatment or changes in lifestyle (e.g., diet, exercise).
• Virtual drift happens when the prediction model’s performance decreases over time, even though the target variable and input data stay the same. This can happen for various reasons such as changes in the technology you are using or the accumulation of unexpected patterns in the data.

Data drifts — or as implied, changes in the statistical properties of a dataset over time — can negatively impact the performance of a model. To maintain data quality, it is important to continuously monitor these drifts and alert relevant stakeholders when they occur. There are various methods for this purpose such as:

• Data Visualization: A straightforward way to detect data drift is by visualizing the data over time using plots such as histograms, scatterplots, or boxplots. You can tell if there are significant data distributions by observing these plots.
• Statistical Tests: Tests such as the Kolmogorov-Smirnov test, Anderson-Darling test, chi-square tests, t-tests, and population stability index assess if the data distribution has changed significantly formally. These tests can be run periodically to detect data drift. There are also specific algorithms that can be used to identify changes in the statistical properties of a data distribution, which may indicate data drift. These algorithms can be grouped into four main categories: change point detection, statistical hypothesis testing, clustering, and outlier detection.

Data validation is checking the data to confirm it meets specific criteria or constraints (i.e., data is complete, accurate, and consistent). Quality checks include evaluating the data so that it is suitable for a particular purpose (i.e., training a Machine Learning model). There are several ways these two ensure data quality:

• Before the data is used to train a model, it is often necessary to clean and preprocess the data so that it is in a suitable format. This can include filling in missing values, removing outliers, or scaling the data.
• Data integrity checks are used to detect and correct errors in the data such as duplicates, inconsistencies, or invalid values.
• By regularly monitoring and evaluating the performance of a model on a validation or test set, you can identify when the model’s performance begins to degrade, which may be an indication of drift in the data.
• Several data quality metrics across the dimensions of Completeness, Uniqueness, Consistency, Accuracy, Validity, and Timeliness (e.g., percentage of missing values, percentage of unique values, inter-rater reliability, F1 score, Cohen’s kappa) can be used to measure the quality of a dataset. By regularly tracking these metrics, you can identify when the data quality may be decreasing and take action to address the problem.

Model monitoring with data validation and quality checks helps improve the performance and reliability of models by ensuring that the data used for training and evaluation is high quality.

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Using human feedback to identify errors made by the model in the monitoring process is an effective approach. As implied, humans review the model’s output and provide feedback on any errors or mistakes they identify. This feedback can then be used to fine-tune the model and improve its performance. It’s essential to have a systematic process for collecting, storing, and incorporating this human feedback. A simple system for collecting user-reported errors, such as a form, can be effective. It is also essential to have feedback from a diverse group to provide various perspectives. In some cases, expert knowledge from a specific individual may also be necessary.

Keeping a record, especially on issues and resolutions, is crucial. This practice can help identify trends and improve the model over time. Maintaining a record of the model monitoring process is important for several reasons:

• It allows you to track the performance of your models over time and identify any trends or patterns that may emerge. This can help you identify potential issues early on and take corrective action before they become major problems.
• The record can be used to document the steps taken to improve model performance and reliability. This can be useful for demonstrating the effectiveness of your efforts to stakeholders and for continuous improvement of your models. It also helps you maintain a history of the model (model lineage). This strategy can help debug and understand how the model evolved.
• Keeping a record of the model monitoring process can help you identify best practices and lessons learned, which can be applied to future projects.

In conclusion, model monitoring is an essential aspect of data science as it affirms your model’s accuracy and reliability. Following the tips discussed in this article guarantees that your ML models are performing well and that your data is of high quality. These tips help you get the most out of your ML efforts and build efficient and reliable systems.