Model performance is a measure of how well your Machine Learning model performs a task during training and in real-time deployment. As ML engineers, we define performance measures such as accuracy, F1 score, Recall, etc that show a comparison of the model’s predictions with the (known) values of the dependent variable in a dataset.
Continuous tracking and monitoring of these performance metrics are critical in achieving your desired goals. One effective way of doing this is using ML monitoring and observability tools, which are already proven methods to see if your models are performing as intended.
Other than tracking, these monitoring and observability tools also help to improve model performance. But how?
- By giving you insights into how well your model performs in production.
- Alerting you when issues arise e.g concept drift, data drift, data quality issues, etc.
- Offering you tools to investigate and remediate these issues.
- Providing you with insights into why your model is making certain predictions and how to improve model performance.
With these insights, Data scientists and ML engineers:
- Can make better decisions about what updates are needed to increase accuracy for Machine Learning models in production.
- They can respond better to issues when they arise since they already know where the issue is coming from.
What is ML Model Monitoring?
ML model monitoring is a set of techniques employed to observe ML models in production and ensure the reliability of their performance. ML monitoring starts from data collection and continues when the model is in production.
ML models are monitored in real-time with ML model monitoring tools. The wide range of insights gained from these tools can be invaluable when it comes to improving your overall data science analysis process for model prediction and model behavior.
After training on a static set of examples in development, ML models in production perform inference on changing data from a changing world. This discrepancy between static training data in development and dynamic data in production causes the performance of a production model to degrade over time. Essentially, ML Monitoring comes in as a set of techniques to observe ML models in production and ensure their performance reliability.
What is ML Monitoring Used For?
In life, as well as in business, feedback loops are essential. The concept of feedback loops is simple: You produce something, measure how it performs, and then improve it. This is a constant process of monitoring and improving. ML models can certainly benefit from feedback loops if they contain measurable information and room for improvement.
Consider that you trained your model to detect credit card fraud based on pre-COVID user data. During a pandemic, credit card use and buying habits change. Such changes potentially expose your model to data from a distribution with which the model was not trained. This is an example of data drift, one of several sources of model degradation. Without ML monitoring, your model will output incorrect predictions with no warning signs, which will negatively impact your customers and your organization in the long run. Find out the 5 most common reasons your ML model may be underperforming in production.
Model building is usually an iterative process, so monitoring your model by using a metric stack is crucial to perform continuous improvement as the feedback received from the deployed ML model can be funneled back to the model building stage. It’s essential to know how well your model performs over time. To do this, you’ll need monitoring tools that effectively monitor the model performance metrics of everything from concept model drift to how well your algorithm performs with new data.
Why Should I Monitor My Machine Learning Models?
Several steps are involved in a typical ML workflow, including data ingestion, preprocessing, model building, evaluation, and deployment. Feedback, however, is missing from this workflow. Therefore, an “ml model monitoring” framework’s primary purpose is to provide this feedback loop post-deployment, which feeds into the model building phase. This allows the machine learning models to continuously improve themselves by either updating or using an existing model.
Various model metrics should be tracked and reported for issues that arise in production. Impactful and complex issues may not always be noticeable until it’s too late and the performance declines significantly. Hence, monitoring your model throughout its lifecycle will allow you to fix issues relating to your model’s algorithm, environment, etc. before they can have a significant impact on the overall accuracy of your model. Here are examples of just some of the challenges and why you should choose to monitor them:
|Production Challenges||Key Questions||Proposed Solutions|
|1||Data distribution changes||Is there a reason why my feature values suddenly changed?||Add a monitoring system to your ML workflow|
|2||Training-Serving Skew||Despite our rigorous testing and validation efforts during development, why does the model not produce good results in production?||Production environment might be different from the training environment. Try reproducing the production environment in training.|
|3||Model/Concept drift||Why did my model perform well in production but then drop suddenly over time?||Add a monitoring/observability system to your ML workflow to pinpoint the problem.|
|4||Black Box models||How can my model be interpreted and explained to relevant stakeholders according to the business objective?||Add an explainability method or tool to your ML training and production lifecycle.|
|5||Pipeline health issues||Why does my training pipeline fail when executed? Why does a retraining job take so long to run?||Add a monitoring and observability system to your pipeline. Check for bottlenecks, bugs, etc.|
|6||Underperforming System||What causes my prediction service’s high latency? Why is the latency of my different models so different?||Better model environment, increase compute resources.|
|7||Data Quality Issues||How can I ensure the production data is processed the same way as the training data?||Use centralized storage and ensure you monitor it continuously. Make sure training data and production data come from the same source.|
Improving Model Performance
Monitor your model
This is arguably the most important step when it comes to improving a model’s performance. It involves monitoring and comparing all performance metrics case by case and correctly measuring the impact of each feature on the overall model performance.
It notifies you of bugs and drifts in your system such as concept drift, data drifts, etc.
Data drift and concept drift are the primary causes of model drift, making it crucial to detect data and concept drift.
Also, monitor the model’s hardware environment e.g RAM, GPU, Storage, etc.
Add ML Observability
ML Observability gives us insights into the model’s performance. From monitoring our model lifecycle – development to deployment – we get insights into the data, the training, and the environment of the model.
Through model observability, we can perform root cause analysis by setting baselines from training, validation, or prior time periods in production and compare shifts to root cause performance degradation to understand why the model’s metrics, input data, etc are behaving in a certain way, and how they impact the overall performance of your system.
Observability helps us uncover technical debt in our production system that might cause bottlenecks as the model scales up.
Add Explainability to your model
Having an explainability system in place for a model is another way to improve the model’s performance, especially black-box models like deep learning models.
Explainability gives you leverage over these kinds of models and allows you to understand why and how your model works, helping you know exactly what to fine-tune and optimize.
Try multiple algorithms on your model
Testing out different algorithms on your model and comparing multiple algorithms is a good way to improve model performance because by testing out different algorithms you can find the algorithm that is better suited for your datasets and gives you the best result.
To do this efficiently you’ll need to track and monitor how each algorithm performs. We can choose to train them individually or all together and find the one with the best performance.
Tuning the hyperparameters of your model is another common approach to improving the model’s performance. This is because the process involves finding the right set of parameters that helps the model achieve maximum performance. There are several hyperparameter tuning or optimization techniques such as Grid search, Random search, etc that can be used to get optimal parameters for the model.
Increase compute power
Machine learning techniques in general require a high level of computing resources to run optimally. For example, training a deep learning model with a CPU would give you sub-optimal results but when the model is trained on GPU or TPU, the training time reduces and the model performs better.
Add more training data
A model is usually as good as the data it was trained on. So to improve model performance you’ll have to provide more training data samples to the algorithms. The more data it learns from, the more cases it is able to correctly identify.
Why ML Monitoring Tools are Important
Using ML model monitoring tools is one of the easiest ways to ensure things go smoothly. By automating and optimizing the monitoring process, you’ll save time. When running two models simultaneously, you can evaluate their performance, see how they relate to input data, and perform advanced tests.
ML Monitoring tools also are available for both supervised and unsupervised algorithms, allowing you to gain insight into how the accuracy of your algorithm changes over time.
One important component of an ML monitoring tool is the alerting system. This system alerts the ML engineer or data scientist on issues before they arise or as they arise. Having real-time insight into what is happening with your models makes it easier to exchange ideas, thoughts, observations, and spot errors.
Taking advantage of monitoring tools like Aporia that provides real-time drift detection, observability, explainability, and monitoring you can improve the accuracy of your model’s performance and make better decisions when issues arise or when updates are needed.
Overall, monitoring machine learning models throughout their entire lifecycle is vital in improving the outcomes of the model and the overall performance of the model’s system.
Having the ability to view how your algorithm performs in real-time lets you make better decisions about your artificial intelligence models and the process of training them.