The IT revolution has made machine learning model development ubiquitous in all major industries as a missing-critical investment. The Machine Learning lifecycle does not end with the ML model development as it is still under development. The model deployment and monitoring processes ingestion can improve ML Model. For example, data science teams have to monitor or maintain deployed models for at least three years of a product lifespan to ensure the company is getting the maximum value, which is considerably costly. Before we start explaining the techniques to achieve continuous Machine learning model monitoring, let’s first understand this Model Monitoring term in detail:

What is Model Monitoring?

Model monitoring is an operation and maintenance stage of the machine learning development lifecycle, which executes after the model deployment is done successfully. This process determines potential crashes, errors, and latency, and most importantly, maintains the performance level of the machine learning model. To understand this model in simple terms, consider this process as the routine car checkup procedure where mechanics check whether a car is getting periodic oil transfusion or not. Likewise, machine model monitoring is a regular operational task ensuring the efficiency of your model. Today’s intense market competition shows that periodic model monitoring is not sufficient. Because it requires real-time monitoring to detect potential performance issues every year, where even a tiny bug can scatter users’ experience or perhaps a company can lose its user base. You should start with considerations of driving effective machine learning model monitoring techniques:

Achieving Model Accuracy:

The machine learning model monitoring process keeps track of real-time accuracy, which is necessary. It identifies four primary surveys regarding model accuracy, model authenticity, use case, and comparing right and wrong answers. Maintaining model performance accuracy is necessary, and for that, there is a requirement for retraining the ML model more frequently, like on a monthly or yearly basis. Even after getting these many regular check-ups, models may experience a nominal accuracy decrease with time. Such accuracy problems occur when developers apply the same hyperparameter tuning as used at the initial phase, which sounds appropriate at that moment. Additionally, the sudden change in accuracy indicates ongoing changes in parameters, which may be on the positive or negative side. Model accuracy cannot be achieved in all cases due to:

Model Intervention:

When an ML model declares its prediction, it often takes significant actions to make assumptions. For example, you predict the customer demands and set a bar where you would apply the best practices to make it possible. After a few months, when you assess customers’ needs, you can see variations and believe something has gone wrong, maybe with the prediction or marketing actions. You can eliminate this model intervention by using a small subset of accurate data instead of prediction data. Even if it allows you to operate on computing accuracy metrics, it may result in the degraded performance of the model or revenue loss. In some extreme cases, it is not even possible to conduct. I.e., Fraud of credit card detection.

Gap Between Truth and Prediction:

In some applications, you may find a gap or delay in the time while making a prediction. For example, someone reports credit card fraud or being stolen after three months, representing the gap between the prediction and truth. If the model is accurate and performs transactions in a real-time manner, there are fewer chances of knowing about the fraud after a delay of 3 months.

Human Labelling:

You can do manual labeling using computer vision applications or NLP models. Sometimes ground truth labels are automated unless a human interferes. These factors prove all-time accuracy, for which you need to choose some alternatives consistently monitoring machine learning models.

Output Distributing:

The best alternative to the accuracy approach is the output distribution. It counts or summarises outputs of the ML model achieved by selected model inputs. Further, it stores and distributes all outcomes in the classification model. If a developer uses the Regression model, the developer considers that the output distribution process can handle complex distribution and analyze data in more quantity by making the process simpler. This metric indicates the system and user whenever it finds significant changes in the previous model of output distribution. Only two factors are responsible. The IT approach or model is completely changed. The final ML model works correctly and should be ensured. This approach identifies the model's accuracy, analyses output entry behaviors, and indicates developers for further investigation if necessary.

Conclusion:

Considering all these issues and complexity, you must design a monitoring strategy before deploying any ML model. After the model deployment process, you need to track all variables' input and output using machine learning software monitoring frameworks. However, in some cases, like Computer vision and Natural Language Processing (NLP). Here often, frameworks fail to monitor ML models. You can use domain classifiers to drift the identified concepts to make it possible. You can also appoint third-party Machine Learning services providers, like CloudStakes Technology Contact us now to know more about our AI/ML offerings.