Machine learning (ML) is an essential tool for every modern company. It provides businesses with information on the latest trends in customer behavior. It also explores operational patterns for businesses.  

This information helps businesses develop new products that meet the needs of their customers. So, ML applications must provide accurate information to companies.

That's why model monitoring is a crucial tool for all data scientists. Model monitoring, or ML monitoring, assesses the performance of your ML models to determine if they operate efficiently. 

Your ML model performance needs this AI monitoring to ensure the best outcomes for your business. If you're unfamiliar with this system, don't worry! We'll explore how model monitoring works in the guide below. 

Why Do Machine Learning Models Degrade Over Time?

The primary reason that these models degrade with time is changing input. Your input data could change for two primary reasons. 

First, the environment that the ML predicts changes frequently. As a result, the model must adapt to the new environment. Second, the operational data in the pipeline may change over time. 

Environmental Changes

ML algorithms predict future outcomes or adapt processes based on input data. When businesses establish the model, they input the baseline data that helps it run. 

As a result, your ML algorithm will solve your business problems using the values of that time. Unfortunately, businesses and their environments frequently change. As a result, your parameter values become outdated.

Changing Operational Data

From time to time, a business' operational data may change. This change sometimes happens without the engineering teams' knowledge; often, the engineering team has minimal control over where data input comes from. 

The business's dynamics could also affect this. Sometimes, new business decisions can cause operational data to change. Regulations might also force a change. 

Let's imagine a company in Scotland imports goods to the US. Scotland uses the British pound as its currency, which fluctuates at different rates compared to the US dollar. 

Let's say that in five years, Scotland adopts the Euro instead. Changes like this could affect the original ML model performance for the company.

The Components of ML Monitoring

So, how can an AI monitoring system prevent these issues from happening? Before discussing this, we first need to examine the three components of model monitoring to understand how this works. 

First, there's performance analysis monitoring. This component conducts frequent checks on model performance. These checks may run either daily or hourly. 

As they run, these checks perform several critical tasks. These primarily focus on gauging accuracy. 

Second, there's the drift monitoring component. These features compare data distributions, numeric and categorical features, predictions, and real-life results. Finally, there's the data quality monitoring component. This portion offers real-time data checks, predictions, and actual results. 

How do all of these function in practice? We'll explore some of the tasks these components perform down below. 

Comparing Predictions to Outcomes

The first thing your ML model monitoring should do is compare the ML model predictions with real-life outcomes. This way, you can tell how accurate the model's predictions are. A significant gap between the two usually indicates that the ML model needs systematic updates. 

However, accuracy is not a one-dimensional quality. Several factors underlie this metric.

So, a model monitoring system should examine where the model underperforms. A modeling system should scan across several cohorts and fragments of predictions. This way, you can isolate the precise factors that throw off your model's accuracy.

Monitoring Data Drifts

The term "drift" refers to changes in data distribution over time. This data measures the inputs, outputs, and actuals present in a model. 

It's natural for models to experience drift over time as information changes. Some circumstances may cause hard failures because of significant data drifts. This drift results in a model's overall performance becoming worse. 

Remember, models aren't stationary. It's critical for an engineering team to keep models up-to-date with the latest information.  So, keep the best drift monitoring strategies in mind. First, make sure you monitor:

• Prediction drift
• Concept drift
• Data drift
• Upstream drift

This way, you can guarantee your information stays updated. So, spend time monitoring these factors using a few analysis techniques. Techniques like PSI or K-L Divergence can help you quantify any drift. 

Machine Learning Monitoring for Data Quality

Machine learning models don't remain static. Instead, they focus on data and depend on it to make the best possible predictions. So, businesses must discern any data quality issues to determine how your data quality impacts your model performance.

How do you monitor this quality? As data flows through the model inputs, your monitoring ensures it offers the best quality data. This way, as the model ingests and digests the data, it can avoid performance degradations. 

From there, you can utilize data quality monitoring to analyze hard failures. This approach preserves the quality of your data pipeline.

More specifically, search for any of the following:

• Cardinality shifts
• Missing data
• Data type mismatch
• Out-of-range 

Identifying these factors helps you isolate data quality issues. Ideally, your data quality monitors should look upstream and calibrate the exact parameters for your model.

This occurrence should happen regardless of your model version or dimension. This way, you can determine where any features, predictions, or actuals fail to meet their expected ranges.

Work With Model Monitoring Services to Eliminate AI Failures

As you can see, model monitoring services offer several benefits for a business. However, engineers don't have to work on ML monitoring services alone. 

Instead, your engineering team can work alongside other businesses that specialize in AI monitoring. We work hard to ensure that our clients never experience AI failures as long as they work with us. 

If this appeals to you, then excellent! Contact us today to request a demo of our services.