The performance of machine learning models can degrade over time due to changes in data distribution or other external factors. This makes it difficult to monitor machine learning models regularly to ensure they continue to perform as expected. In this article, we will explore Machine Learning Monitoring including the importance of machine learning monitoring, what to monitor in a machine learning model, and how to select the best monitoring tool.
What is Machine Learning Monitoring?
Machine learning monitoring is the process of tracking and analyzing the performance and quality of machine learning models in production environments. It helps to ensure that the models are delivering accurate and reliable predictions and to identify and diagnose any issues that might affect their performance.
Machine learning monitoring is an essential part of machine learning operations (MLOps), which is a set of practices that aim to streamline and automate the development, deployment, and management of machine learning models in production. Monitoring helps to ensure that the models are delivering value and meeting business expectations.
Some of the reasons why machine learning monitoring is important are:
It helps to ensure that the machine learning models are delivering accurate and reliable predictions that meet business expectations and user needs.
It helps to identify and diagnose any issues that might affect the model performance, such as data issues, model drift, model staleness, model errors, model bias, etc.
It helps to prevent or mitigate the negative consequences of poor model performance, such as poor user experience, loss of revenue, loss of trust, or legal or ethical risks.
It helps to trigger actions to improve the model performance, such as retraining, updating, or replacing the model when needed.
What to Monitor in Machine Learning Model?
When it comes to machine learning, monitoring is a component of ensuring that models remain accurate and effective over time. But what exactly should you be monitoring? There are several key areas that require attention in order to ensure that your model continues to perform as expected. Some of them are:
1. Model performance: This refers to how well the model is achieving its intended objective, such as accuracy, precision, recall, F1-score, AUC-ROC, etc. These metrics can be computed on historical or live data and can be compared against a baseline or a threshold. Model performance can help detect and diagnose issues such as model drift, model staleness, model errors, model bias, etc.
2. Data quality and integrity: This refers to the quality and consistency of the input data that the model receives, such as completeness, validity, timeliness, uniqueness, etc. These metrics can help detect data issues that might affect the model performance, such as missing values, outliers, anomalies, etc.
3. Data and target drift: This refers to the degree and direction of drift that occurs in the input data or the underlying phenomenon that the model is trying to predict. Data drift occurs when the distribution or characteristics of the input data change over time. Target drift occurs when the relationship between the input data and the output variable changes over time. These metrics can help identify when the model needs to be retrained or updated to adapt to the changing environment.
4. Embedding analysis: This refers to the visualization and exploration of the latent features that the model learns from the input data. These features can help understand how the model represents and clusters the data, and how it relates to the output predictions. Embedding analysis can help detect issues such as data quality problems, concept drift, or model bias.
How Machine Learning Model is different?
When it comes to software development, we typically follow a structured process where we plan, design, build, and test our applications. We create logical rules and algorithms that the application follows, and we write automated tests to ensure the software performs as expected.
However, the development process for machine learning models is different. The power of machine learning lies in its ability to generalize from historical experience and react to new unseen data without explicitly describing each case. Instead of hard-coding logical rules, data scientists use algorithms that learn relationships between input data and what they wish to predict. These probabilistic rules describe how to transform input data, such as an image, into a prediction, such as the category of objects within an image.
This difference in the development process means that machine learning requires a different approach to testing and validation. In traditional software development, we can write automated tests to ensure the application performs as expected. However, in machine learning, we cannot explicitly describe every case, and the models are constantly changing and updating as new data is introduced.
Therefore, testing and validation of machine learning models is an ongoing process that requires continuous monitoring and adjustment. This process involves testing the model against new and unseen data, monitoring its performance, and adjusting the model's parameters and algorithms to improve its accuracy.
The development process for software applications and machine learning models differs significantly. While software development requires explicit logical rules and automated testing, machine learning models learn from historical data and require continuous monitoring and adjustment to improve accuracy.
Machine Learning Model Monitoring using Metrics and Logs
Machine learning model monitoring metrics and logs are two types of data that can be used to track and analyze the performance and quality of machine learning models in production environments.
Metrics are numerical values that measure some aspect of the model’s performance, such as accuracy, precision, recall, F1-score, AUC-ROC, etc. Metrics can be computed on historical or live data and can be compared against a baseline or a threshold. Metrics can help detect and diagnose issues such as model drift, model staleness, model errors, model bias, etc.
Logs are textual or structured records that capture the events and activities that occur during the model’s execution, such as input data, output predictions, errors, warnings, exceptions, etc. Logs can provide detailed information about the model’s behavior and context and can help troubleshoot and debug the model when it fails or behaves unexpectedly.
Consider the table below for each monitoring checkpoint to decide whether you should generate metrics or collect logs for them.
Factors | Metrics | Logs |
|---|---|---|
Information | Trends | Events |
Context Provided | Poor, Few dimensions | Rich, High number of dimensions |
Analytics | Statistics, Dashboards | Log Analysis: Required tooling |
Generation and Storage Cost | Low, Constant (issues with high cardinality metrics) | Can explode |
Machine learning model monitoring metrics and logs can be collected and analyzed using different tools and techniques, such as MLflow, Azure Machine Learning, Zebrium, Deepchecks, etc. These tools can help visualize and explore the metrics and logs and also provide alerts and recommendations for actions to improve the model performance.
Select the Right Monitoring Tool
To automate monitoring, you need to introduce monitoring agents into your workflow. When you implement monitoring agents, you can choose the monitoring tools of the platforms you use or external monitoring services.
Some of the key considerations to select the right monitoring tool are:
The type and scope of data that you need to collect, analyze, and visualize, such as metrics, logs, events, traces, etc.
The ease of implementation and integration with your existing systems, platforms, tools, and workflows.
The features and functionalities that you need to monitor your system effectively, such as dashboards, reports, alerts, root cause analysis, recommendations, etc.
The scalability and reliability of the tool to handle your current and future data volume, velocity, and variety.
The cost and value of the tool in terms of licensing fees, maintenance costs, support services, and return on investment.
The security and compliance of the tool with your data privacy and governance policies and regulations.
Conclusion
By regularly tracking key performance indicators such as data quality, model performance, and external factors, you can ensure that your machine learning model continues to perform as expected over time. By developing a robust monitoring strategy that includes both automated and manual checks, you can catch issues early and take corrective action before they negatively impact your business or customers. So, make monitoring a priority in your machine learning projects to ensure their long-term success.