According to a venture beat study, only 13% of the data science projects make it to production. But deploying your machine learning models to production is not the final step, monitoring the model is very important to understand the behavior on the live data.
The quality of data feeding to your ML model might drop because of changes in your upstream data pipeline or anomalies. Consider an example where a bug in the data pipeline causing a field to be processed as null or empty. It is easier to find if that field is empty all the time. But what if that field is empty on some conditions and fine in some conditions. You need to keep track of missing fields, data type mismatch, etc.
Your model is as good as the data it is trained on. Machine learning models are trained on a set of data and that dataset has particular statistical characteristics. Over a period of time characteristics of your live data will change from training data. This can be because of a change in business offerings or changes in customer behavior. You have to take appropriate actions to avoid performance decay because of the drift.
Machine learning models predict based on what it saw during the training time. As your model keeps on seeing new data, performance will degrade. You have to retrain your model when the data drifts or performance degrades.
It is very hard to find a balanced dataset, often we will end up with an unbalanced dataset for building a model. This can lead to bias in the model predictions. Also, if the live data is skewed towards one class then also your model might show a bias. It is very important for businesses to ensure the models are unbiased. Biased models can cause serious damage to a company’s reputation.
Most of the machine learning models are black-box in nature. Understanding the model predictions and inner working of it is very essential. If you are not sure why the model is predicting in a particular way, you can’t trust the model. Regulations like GDPR and CCPA give rights for consumers to ask for an explanation for your model’s prediction. Take an example where someone is denied a loan based on a machine learning model’s output, the consumer would obviously like to know the reason for it.
One way is to manually check the data and predictions every week or month. But this will take a good amount of time from the Data Scientist or ML Engineer to regularly audit. Taking a reactive measure when the model fails can have a big impact on the business and might take a lot more time to find out the issue.
We have developed a Machine Learning Model Monitoring tool to tackle all these problems proactively. Request for a free demo of our product today and discuss your use cases with our experts.