Council Post: Measuring Accuracy Of AI Solutions In Production

As industries embrace Artificial intelligence (AI) in key decision-making processes, it’s important to ensure that the accuracy of these AI solutions in production is sustained. While measuring accuracy offline with testing datasets is relatively straightforward, doing so in a production environment presents unique challenges. In this article, we will explore the need for measuring accuracy in production, challenges associated with the same and best practices to address those challenges using examples.

Need for measuring accuracy in production
Measuring the accuracy of AI solutions in production is crucial as accurate AI solutions have a direct impact on business outcomes and ROI. AI-infused products can deliver the best user experience as we see in retail and e-commerce. However, an inaccuracy can overturn this same experience; in fact the consequences of inaccuracy are critical in industries such as healthcare and automotive. AI models can drift over a period due to changing data and environmental factors. There is no guarantee the model results will stay consistent throughout. Therefore, having the ability to measure the accuracy of models in production at regular intervals is necessary to take corrective actions.

Challenges of measuring accuracy in production
Measuring accuracy comes with a few challenges in terms of data variations, scalability and labeling. Unlike test datasets (used to calculate model accuracy during product development), production data can be complex, dynamic and diverse. The data may contain noise, anomalies, and unexpected edge cases, making it challenging in a production environment. Additionally, enterprise production environments often involve handling massive datasets and high throughput so measuring accuracy at this volume and scale can get challenging. Furthermore, in production, obtaining accurate labels/ground truth for data to evaluate models is challenging. For real-time data use cases, this can be further challenging as it is time sensitive i.e., by the time the data is labeled for measuring accuracy the AI solution would have already created multiple errors impacting the solution.

Let’s take the example of computer vision-based AI solutions. Consider an AI solution to recognize objects using security cameras. In a controlled test environment, the model may outperform with higher accuracy, however, when deployed in production may face several challenges.