The Orca blog

Orca’s RAC text classifiers adapt in real-time to changing data, maintaining high accuracy comparable to retraining on a sentiment analysis of airline-related tweets.

Memory-based updates enable an image classifier to maintain near-perfect accuracy even as data distributions shifted—without the need for costly retraining.

Orca's vision for the future of AI is one where models adapt instantly to changing data and objectives—unlocking real-time agility without the burden of retraining.

Orca's survey of 205 engineers revealed that data challenges remain at the forefront of machine learning model development.

ML models in production often face unexpected use cases, and adapting to these can provide significant business value, but the challenge is figuring out how to achieve this flexibility.

ML models must be adaptable to remain effective as business problems shift like targeting new customers, products, or goals. Learn how Orca can help.

Orca can help models adapt to rapid data drift without the need for costly retraining using memory augmentation techniques.

Detecting toxicity is challenging due to data imbalances and the trade-off between false positives and false negatives. Retrieval-Augmented Classifiers provide a robust solution for this complex problem.

When evaluating an ML model's performance, the definition of "correct" can vary greatly across individuals and customers, posing a challenge in managing diverse preferences.