From training to fine tuning, it takes so much effort to develop a machine learning model that many businesses make the mistake of ending the process at the AI model’s deployment stage.
But even if your current results are excellent and your customers are giving you great feedback, don’t forget to look beyond deployment and enforce regular monitoring and maintenance protocols.
Machine learning models can still degrade in performance over time, and organizations that fail to provide an ongoing maintenance effort will suffer from poor predictions, frustrated clients, and other losses.
What Is Model Drift and How Does It Happen?
AI model management will always be necessary thanks to a phenomenon known as model drift. AI originates from a set of historical data, the initial dataset ML developers use to create the algorithm.
If the algorithm’s input data never changes in nature, then model performance will always be consistent. However, the real world is a dynamic environment where input data can change for any number of reasons. An AI’s predictive power can degrade if it begins reading unfamiliar datasets.
This model drift has occurred in the past. In the aftermath of COVID-19, for example, many banks across the world saw inaccurate predictions from their AI deployments that had previously performed excellently. Financial activities went through a paradigm shift at the time since everyone was recalibrating to lockdowns.
But not all instances of drift are this clear-cut. AI model maintenance can easily run into several challenges:
- Type of drift– Drift can occur when the input data changes distributions, but it’s also possible for concept drift to occur, where the relationship between the input data and intended output changes over time. The resulting dip in performance will call for model retraining.
- Data outliers– Extenuating circumstances and unplanned situations make it difficult to pin down the exact cause of degrading ML performance.
- Data quality issues– Quality control for the input data must be both robust and consistent. Many models operate on two datasets: the original training data and current production data. Both must be clear of quality control problems.
- Intentional sabotage– Some industries are at risk of concerted attacks against predictive models. In the financial sector, credit card fraudsters often intentionally look to bypass models that detect suspicious transactions.
Because ML models are typically black boxes, it’s not always easy for model maintenance staff to interpret the output. For this reason, part of model maintenance is keeping the algorithm in line with business objectives through regular performance monitoring and retraining efforts.
Solving Performance Degradation with Model Maintenance
A model maintenance team is responsible for not only detecting whenever ML performance degrades but also when and how to retrain the model with new datasets. This regular optimization is always necessary no matter the industry or use case.
When To Retrain AI Models
The frequency of retraining depends on the nature of the input data, the purpose of the model, and the type of AI in question. Some models need retraining only sparingly, while others need to adapt rapidly.
Maintenance teams have the choice between time-based and continuous retraining. The time-based methodology retrains the model at a regular interval and does not consider model performance. This option makes sense if you understand your environment and how often its variables change.
A continuous approach is a more hands-on one. It bases retraining frequency on certain key performance indicators (namely accuracy thresholds) and retrains whenever performance falls below acceptable levels. Maintenance teams that take this option must know how to measure model performance accurately.
It’s also not uncommon for businesses to take a hybrid approach, supplementing regular retraining with performance monitoring.
How AI Model Monitoring Works
Performance monitoring is a proactive measure for detecting problems in a machine learning deployment before they cause consequences for your business. Maintenance teams set up monitoring initiatives to guide future improvements and make the model’s prediction mechanism transparent. With these preparations, it’s easy to demonstrate how a machine learning model provides value to the business.
Performance monitoring starts with the input data. Any data quality issues—including duplicates, missing values, and formatting inconsistencies—will negatively impact the model’s ability to make accurate predictions. But even beyond quality is data drift, a change in the relationship between the training data and the actual data. Maintenance specialists often measure the statistical properties of production data, including its deviations, frequency, and averages.
The other side of model monitoring is looking for concept drift, a phenomenon where the relationship between the input data and the correct predictions fundamentally changes. As a result, the labels the model relied on are no longer applicable. Concept drift can happen quickly or gradually, and some shifts may only happen temporarily.
The Role of Model Maintenance in MLOps
Despite its complexity, maintenance is actually just one responsibility in the post-deployment stage of machine learning model management. Experienced businesses invest in Machine Learning Operations (MLOps) teams specifically to support retraining and maintenance procedures.
MLOps requires your attention because it enables:
- Collaboration between the data scientists– Who build the datasets, and the AI developers, who maintain the model in a production environment. MLOps arose in response to the otherwise separate nature of these two functions.
- Feedback– Customers are often among the first to recognize whenever a business’s model is failing. Customer feedback is an invaluable part of model maintenance, and the faster MLOps integrates it into the workflow, the better.
- Management buy-in– Machine learning is still making its way into modern business, and not all upper-level executives understand how AI development and upkeep work. MLOps ensures that management understands how continuous maintenance is required, even years after deployment.
- A retraining pipeline– MLOps is also responsible for building a retraining strategy, which includes the tools and policies necessary for model maintenance.
Machine learning operations teams also look for model monitoring software tools to help with these responsibilities. Because they specialize in analyzing model performance, these tools often feature dashboards, data visualization screens, and built-in metrics and reporting features.
More advanced platforms also have integrations—such as those with databases, APIs, and file transfer systems—to encourage the smooth movement of data.