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Machine Learning Monitoring: A Cross-Disciplinary Endeavor
Monitoring is a cross-disciplinary endeavor, meaning that different professionals may have different interpretations of what it entails. For data scientists, it may mean one thing, while for DevOps and business professionals, it may mean another. That's why, when it comes to monitoring, there are several key areas we focus on: incoming data, machine learning models, and serving infrastructure.
Machine learning is powerful, but it's not infallible. Machine learning is only as good as the data it's trained on and the environment in which it operates. If you're relying on machine learning models to make decisions, you need to know when those models are no longer accurate. Monitoring can help you catch those issues early.
Definition
Machine learning monitoring refers to the continuous tracking and evaluation of machine learning models to ensure that they are performing optimally and producing accurate and reliable predictions. It involves monitoring the input data, output data, and the performance of the model over time to identify any changes or anomalies that may affect its accuracy or reliability.
Most people associate it with only data and models, but there is much more to it than just those components.
In addition to monitoring the performance of the machine learning model itself, it is also crucial to monitor the code and the serving infrastructure to ensure that the model is functioning correctly and efficiently.
Example
Let's consider a fraud detection model used by a financial institution. The model is trained on historical data to identify fraudulent transactions and provide alerts for investigation. After deployment, the model's performance needs to be monitored to ensure that it is correctly flagging fraudulent transactions and not producing too many false positives or negatives.
To monitor the model, the institution could track:
the number of transactions flagged as fraudulent,
the number of actual fraudulent transactions identified, and
the number of false positives and negatives over time.
They could also monitor the data inputs, such as:
the types of transactions being processed and
any changes in the input data and prediction data distribution.
By monitoring the model regularly, the institution can identify any changes in the data or model performance and make necessary adjustments to maintain the model's accuracy and reliability.
In addition to monitoring the performance of the machine learning model itself, it is also crucial to monitor the code and the serving infrastructure to ensure that the model is functioning correctly and efficiently.
The code that runs the model and handles the data inputs and outputs should be monitored to ensure that it is functioning correctly and efficiently. Any errors or bugs in the code could lead to inaccurate results or a breakdown in the model's functionality.
Furthermore, the serving infrastructure that hosts and runs the model should also be monitored. For example, the infrastructure's CPU usage, memory usage, and network latency should be monitored to ensure that the model is running efficiently and without interruptions. Any issues with the infrastructure could cause the model to slow down or fail, which would affect the performance of the model and lead to inaccurate results.
To address these concerns, the financial institution could set up monitoring and alerting systems that track the performance of the code, the infrastructure, and the model itself. These systems could send alerts to the relevant teams if any issues are detected, allowing them to take quick action to resolve the problem and minimize any impact on the model's performance.
Overall, monitoring machine learning models is a complex process that requires ongoing attention to many different factors. By monitoring the model, the code, and the serving infrastructure, organizations can ensure that their machine learning models are performing optimally and producing accurate and reliable predictions.
With a monitoring solution, you want to provide an overview of all the components involved in the machine learning process. This will enable business users to track project progress, identify areas for improvement, and ensure that deliverables are reliable. Of course, it's not just about the business side of things. A monitoring solution will also benefit data scientists and machine learning engineers by providing them with insights into project performance and potential issues. If there are any downtime-causing problems, our teams will be able to act quickly and get everything up and running again.
In short, a monitoring solution is an essential component of any successful machine learning project. By providing a comprehensive overview of all aspects of the project, we can ensure that everyone involved is on the same page and working towards a common goal.