Defeat Data Drift - Real World AI Maintainability
Environments with rapidly changing data sets create some of the greatest opportunities for AI models to add value. They're also some of the most frustrating environments to build and maintain models for, and you spend a lot of time supervising retraining runs, rather than pushing for increased innovation.
With Orca, your models can adapt to changing data in real time without time-consuming training processes. Plus, Orca’s Curation tools make it easy to make model changes without ML knowledge, so stakeholders can change data directly without having to hand things off to the engineering/data science teams for day-to-day model updates.
Up-to-date models without the cost and frustration of additional training
The world changes, and your model's training data becomes outdated, causing outdated results. Without a simple way to update the data within the model, keeping your model up-to-date is costly, time-consuming, and annoying.
Orca-augmented models allow for fully dynamic and updateable data. This means you can quickly adapt to data changes. With Orca, you can add, delete, or modify memories in real time, either programmatically or via our UI. This ensures your AIs stay current and reliable.
Fast data updates without handoffs
Orca's data curation tools don't need advanced Machine Learning knowledge. You just need to know the data model.
ML/AI engineers and Data Scientists work more quickly. Product managers, data teams, and stakeholders can update outputs directly. This democratization speeds up corrections, cuts delays, and reduces friction. It also lets AI/ML teams focus on more valuable and interesting tasks instead of routine maintenance.
Dramatically shorter iteration cycles
Not all model changes you might want to test are data changes, but many are. With Orca, you can iterate on data changes in near real time without waiting on training runs.
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