Even high-performing models fail in production. False positives, false negatives, and overlooked edge cases can cost time, money, and trust. Learn how to detect, understand, and fix model failures faster, without labeling everything.

In our upcoming Outside the Bounding Box masterclass, you will learn:

• What edge case detection looks like in practice
• How to detect model blind spots with advanced metrics
• How to improve model performance by labeling the right data
• How to implement the above, including model evaluation and creating high-value samples, on your CV dataset

Whether you’re an ML Engineer working on CV models or leading an AI team responsible for model performance, you’ll walk away with practical strategies to surface errors and prioritize the most valuable samples instead of labeling everything.