No matter what industry you're in, technology plays an important role. That role is only increasing, and part of that technology is machine learning and artificial intelligence.
From fintech to the cloud, AI models help crunch data and inform decision-making processes. The adoption of AI is only growing, with the industry expected to increase at over 40% a year.
As your business grows and relies on these technologies more and more, it's essential to protect against a critical pitfall: data drift, an example of AI failure. If you don't eliminate AI failure, you risk corrupting your data and stalling your business. Keep reading to learn how you can implement data drift detection.
What Causes Data Drift?
The data being fed into AI models may drift over time. This drift is caused by a variety of factors and can result in poor model performance
One of the reasons models encounter drift is changing input or datasets over time. AI models are trained on a particular set of data and quickly churn out results on similar datasets once taught.
However, if you begin to change some aspects of the input, the model will skew results as it does not expect what you're providing it. When this happens, you get unexpected or undesired results.
Even if you don't change the input and your model is working on a trained dataset, the environment can change over time. This means that the relationship between the input data and output data could have changed (this is commonly referred to as concept drift). This change in relationship can result in poor performance and unexpected outcomes.
Detecting Data Drift
Understanding how things work may not feel like a priority until things break. If this is the first time you begin to look under the hood, you're doing it wrong. You need to understand how your models work and how they impact your processes before things go wrong.
Monitor Production Performance
Monitoring your model’s performance in production is one way to catch data drift. If your model is beginning to drift or you've introduced a change of environment, performance studies can detect it.
If the model decays over time, you can catch the issue and retrain your model. Doing so returns the model to good accuracy and doesn't impact the processing pipeline.
Statistical analysis can help you locate changes over time and identify the causes. Whether it's a change in variables or expanding business needs, analysis can determine where your model is decaying.
Monitor Input and Output Data
Another way to detect drift is by monitoring both input and output data flow. If you keep track of what you're feeding the model, it's easier to understand what caused the drift.
If the input data hasn't changed, keeping tabs on the output can help detect areas where the model may need retraining. If only specific processes or data sets are drifting, you can isolate those areas of the model.
Eliminate AI Failure
Understanding how your models work and knowing how to detect failures are significant benefits. On their own, however, they're not enough. You need to eliminate failure points and save your models from data drift.
Test Your Models
One way to prevent data drift and AI failures is to test your models. Everything decays over time, including your models. If you anticipate this and stress test your AI at regular intervals, you can catch issues before you'd otherwise notice them.
Getting ahead of the curve saves time and resources and helps you implement fixes before problems affect your pipeline. Testing your models before reports of data drifts occur ensures your AI processes are well-trained and working correctly.
Automate Monitoring Tools
Implementing data tools that automatically monitor your process ensures you keep tabs on performance. If anything changes too much, automated tools will create reports for you to review.
Open the Black Box
Although it may be tempting to 'set and forget' your AI models, it's essential to document and understand how they work. You should never run them in a fashion where you're unsure of how they're crunching the data or what their functionality includes.
Models are only as good as the data they were trained on. Although every effort is made to ensure a holistic training process, drift will occur. Retraining models may be intensive, but adjusting them to keep pace with changes improves accuracy and resets drift.
Selecting the Best Tool
Relying on increasing integrations with AI and machine learning is critical to the evolving world. But if you don't select a toolset that helps you understand and manage your AI, you can't trust the output.
You need a tool to manage every aspect of your model, from training and testing to monitoring data flow and output. If your current suite of tools can't keep up, it's only time before data drift corrupts your process.
At Robust Intelligence, we focus on creating a toolset that eliminates risk and helps you manage data drift. We've worked with multiple businesses from every industry to help manage their AI models.
Detecting data drift and managing risk is critical to any AI workload. Contact us today if you'd like to see how RIME can manage your machine learning system and prevent data drift.