Real-time adaptability for your models
Orca helps machine learning teams update models instantly as business needs and data change, ensuring peak performance.
Models using Orca handle data evolution more effectively
Build AIs as dynamic as the real world
Instantly expand to new use cases or environments
Keep up with rapidly-evolving data
Customize to different preferences
Adapt to changing business objectives
How does Orca's augmentation methodology work?
Orca is an end-to-end system that integrates data management, models, and automated tuning for classification and other predictive systems. Orca teaches these models to leverage external data during fine-tuning, transforming them into "retrieval augmented" systems that adapt instantly to change. By separating reasoning from knowledge, models can be updated by modifying external data rather than retraining. Orca’s closed-loop nature also links model behavior to individual data points, enabling real-time data debugging and refinement, so you can build and update models with even just a few data points.
from datasets import load_dataset
from orcalib.rac import LabeledMemoryset, RACModel
memoryset = LabeledMemoryset("datalicious") #cloud storage backed dynamic model memory dataset
source_dataset = load_dataset("datalicious/datalicious")
memoryset.insert(source_dataset) #works with all standard dataset formats
my_model = RACModel(num_classes=10)
with my_model.use(memoryset):
my_model.finetune(source_dataset)
my_model.attach(memoryset)
my_model.predict(new_input)
memoryset.analyze() #tell me what could be better about my data
with my_model.use(new_memoryset):
my_model.predict(new_input)
Find out if Orca is right for you
Speak to our research scientists to see if we can help you build more adaptable models.