Continuously updated classification & regression
Traditional deep-learning models lose accuracy post training as training data ages out, new information of high relevance becomes available, and/or business logic changes. While retraining the model can adapt to these forms of data drift, models continue to decay between these often expensive retraining cycles. In contrast, Orca-powered deep-learning AIs continuously (and immediately) update with online edits to the attached memory datasets the models use at each inference. This results in a sustained high level of consistent accuracy which drives step function increases in both performance and efficiency.
Continuous accuracy
Although the degree of inter-training degradation obviously varies by use case, these degradations are often quite significant as is the expense required to mitigate them. One example is an image classifier subjected to data drift - Orca’s continuous updating capability maintained consistent accuracy and showed an 800% percent improvement over the conventional existing approach.
Online learning
In addition to updating Orca memorysets with new data to maintain accuracy, the system can feed inference outputs or downstream feedback back into the memoryset to improve accuracy continuously. This also enables semi-supervised workflows in which a small number of labeled datasets is sufficient to get the model deployed after which labeled data can be added over time to the memoryset. In addition, automated workflows enable bulk data labeling propagation based on system recommendation.
With these continuous updating capabilities, Orca adjusts to new data, learns over time, and agentically steers your model in real time.
Find out if Orca is right for you
Speak to our engineering team to learn how we can help you unlock high performance agentic AI / LLM evaluation, real-time adaptive ML, and accelerated AI operations.