It’s natural to focus on the processes of data preparation, model creation, and deployment of the solution to production while discussing the implementation of machine learning solutions. However, it’s critical for teams to understand that deploying the machine learning model isn’t the end of the process. It can only be thought of as the conclusion of the beginning.
Unlike a standard software application, a machine learning (ML) model’s behavior is not solely determined by its code. In fact, an ML solution’s behavior is significantly influenced by the data it was trained on, and the model’s performance will alter as the data being given to it evolves. We’ve described how monitoring, ongoing labeling, and model retraining can help a machine learning model’s efficiency and accuracy deteriorate.
Challenges of Maintaining an ML model
A standard software product’s behavior is determined by its code, but an ML solution’s behavior is determined by both code and data. As a result, when designing and delivering a normal software product, there is one less aspect to address in the field of post-release monitoring. That’s not to suggest that monitoring isn’t useful in some cases, but the chances of stable functionality in production becoming less dependable if the code isn’t changed are extremely slim.
On the other hand, machine learning models differ significantly in this regard. The model’s reaction is determined by its code as well as the data presented. If the model’s input changes over time, as it almost certainly will, the model will almost certainly not be trained to handle the newer input effectively. As a result, the accuracy of its forecasting ability will deteriorate with time. Monitoring the ML solution is crucial to ensuring that this is detected.
Update Model with changing scenarios
In the maintenance of a machine learning solution, there are several types of drift to consider. When the input to the model changes, for example, data drift happens, affecting the model’s prediction ability. Consider the case where a model is created to detect spam comments in response to videos that have been placed on a website. At first, the model may appear to be very good at spotting spam comments. Those that publish spam comments, on the other hand, may change their strategies over time in order to avoid discovery. The data then drifts, as there now exists input that should be marked as spam but the model is unaware and will not do so. If the model isn’t retrained to evolve with these tactics, a higher percentage of spam comments will go undetected.
When the interpretation of the evidence changes, drift happens. A change in the classification space is an example of this. Consider the case where a model is created and trained to identify whether a picture is of a car, truck, or train. However, after some time, the model is fed images of bicycles. Concept drift has happened in this scenario, and the model will need to be adjusted in order to accurately categorize the new photos.
Both of these types of changes result in a deterioration in the predictive capabilities of the model. Therefore, it’s critical to detect instances of drift as early as possible in order to ensure that the service is powered by the model continues to provide value.
Monitoring ML Solution
Priority number one in ensuring model effectiveness over time is monitoring the ML solution. With respect to machine learning, this practice includes tracking metrics involving model behavior to help detect a decline in performance. In the effort to detect potential drift as early as possible, teams can set baselines for model behavior, and when the model begins to stray from that baseline value, they can raise alerts to notify the proper personnel to analyze and remediate the problem.
For instance, let’s consider a machine learning solution that was constructed to detect fraudulent credit card transactions. If the model is typically expected to detect instances of fraud in 0.5% of cases, then this may be a good baseline to set. But what if fraud is detected by the model in 5% of cases all of a sudden? It could be that fraud is occurring with 10x greater frequency, but that probably isn’t the case. Instead, it’s more likely that some new trend in the data has emerged that is impacting the accuracy and effectiveness of the model’s predictive capabilities. Thus, an alert should be raised when the baseline is dramatically exceeded, and then the process of evaluating model performance should commence.
This example shows how companies may keep track of data drift by looking at the distribution of categories applied to production input over time. There is a chance that drift has developed when the frequencies with which classifications are applied are no longer in accordance with what used to happen.
Furthermore, keeping track of the model’s input data and comparing it to the data used to train the model might help identify instances of data drift. When the difference between the training data set and the production input exceeds a certain threshold, the model may need to be retrained to handle major changes in the production input.
This type of monitoring information and its associated alerts provides data engineers, data scientists, and other critical personnel with the level of detail necessary to evaluate the cause of the problem and make the appropriate changes to address it (i.e. re-evaluating the viability of the current model, re-labeling and retraining to regain performance, etc.).
Constant Labeling and Model Retraining
Curating high-quality labeled data to train the model is one of the most crucial parts of creating a successful machine learning workflow. The technique of identifying or annotating groups of samples with one or more labels is known as data labeling. Labeling provides the foundation for an ML model to identify common characteristics from similarly tagged data when done for huge amounts of data. This is critical in the creation of a model that can accurately classify data. A supervised model, on the other hand, learns by doing. The labeled training data serves as “examples” for the model to learn from.
The model evaluates the labels against the data to which they are attached, learning the relationship between the two. It then uses what it’s learned about this relationship to classify new data points in the future. Therefore, it is labeled data that enables a machine learning solution to hone the predictive capabilities that can then be leveraged to accurately classify input in a production environment.
But when input data changes and drift occurs, the model’s understanding of the relationship between the input data and the appropriate label will be outdated, and therefore likely incorrect. When evolving data is determined to be the cause of the decay in predictive capabilities, one potential solution is to re-label large quantities of data in a manner that corresponds with the data’s evolution. The ML solution can then be retrained using the newly labeled data, thereby updating its behavior to regain its effectiveness and accuracy.
TagX – your trusted partner
Data labeling, in its typical form, is an arduous and expensive undertaking. As a time-consuming and manual process, it requires individuals with extensive domain expertise and a team of dedicated data labelers. These factors create a bottleneck in the process of producing high-quality labeled training data. And this bottleneck prevents ML teams from refreshing and retraining their models with any level of efficiency. With that said, there is a need to choose a reliable partner for the task of data labeling. TagX stands out in the fast-paced, tech-dominated industry with its people-first culture. We offer data collection, annotation, and evaluation services to power the most cutting-edge AI solutions. We can handle complex, large-scale data labeling projects whether you’re developing computer vision or natural language processing (NLP) applications. While impossible to prevent, the decay of a model due to changing data can be remediated by constant data labeling by TagX’s team and model retraining.
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