The road to integrating AI into real-world applications is fraught with many unforeseen challenges. These challenges are not just about building AI models but ensuring they perform reliably in diverse and unpredictable environments.
Recently, LatticeFlow’s Co-founder and CTO, Pavol Bielik demonstrated how to uncover blind spots in machine learning models and fix them to improve model performance, emphasizing the need to go beyond aggregate model accuracy.
Here are some key takeaways from Pavol’s talk on ML Ops Community:
Unveiling the Hidden Complexities of AI Models
For machine learning engineers, achieving a 90% accuracy rate during the early stages of model training can be euphoric, especially when the models are trained on large and complex datasets. But what happens when your model’s accuracy hits a plateau and remains stagnant? At this point, you might be asking yourself if aggregate performance is the suitable metric to evaluate your model or if there are alternate methods for testing and improving its performance. If you analyze the remaining 10% inaccuracies represented by the red dots below, you can get deeper level insights into why your models are failing and systematically identify root causes such as; spurious correlations, rare samples, incorrect labels etc.
Identifying and Addressing Model Blind Spots
Model blind spots are areas where models, despite seemingly performing well in training, fail when encountering real-world data. The challenge is , finding these blind spots that are hidden in the first place. Both when the users have a hypothesis they would like to test (such as, zoomed in images or hands in the images), as well as automatically by taking advantage of white-box integration of custom deep learning models (such as, degraded performance over urban areas in satellite imagery).
Real-World Applications and Demonstrations
“It is difficult enough to develop complex AI models, your AI tools should not be part of the problem.”
– says, Pavol Bielik, LatticeFlow’s Co-founder and CTO.
If you look at the datasets below, you’ll notice 98.5% accuracy in one of the samples, yet 4.6% accuracy in the other sample. This is why using aggregate performance can be misleading when evaluating model performance, especially when deploying to production.
LatticeFlow: The AI stack for mission-critical AI
To help enterprise companies scale the development of mission-critical AI applications, we have designed a complete stack from the the ground up. Your team members including data analysts and machine learning engineers can collaborate using a single source of truth and make informed decisions to help deploy safer and more robust AI models to production.
Want to learn more? Click on the link below and someone from our team will reach out to you!
