Which AI models are used for real-time processing?

To maximize processing speed and minimize the risk of failures or delays, our platform uses an intelligent, dynamic method for selecting which AI models to use for real-time processing tasks.

How it works: Instead of using all available models, the system dynamically selects the most essential ones needed for a specific task. This ensures faster, more reliable performance for all real-time operations.

How model selection works

Our AI system has a comprehensive suite of models that work together in a processing block. However, not all of these models are required for every task.

• Real-time Processing: For tasks that require an immediate response, our system intelligently selects a smaller, optimized set of models. This selection is based on the specific requirements of the task at hand, ensuring rapid and accurate results.
• Simulations & Non-real-time tasks: For processes like simulations, analytics, or model training, the system utilizes all models within the processing block. These tasks are not subject to the same strict time constraints, allowing for more extensive, in-depth analysis.

What about the other models? Even if a model isn't selected for a real-time task, its predictions are still generated. These predictions are processed without a real-time constraint, ensuring that all data is eventually processed comprehensively.

What's next

To learn more about our AI models, you can read these related articles:

• How often are the AI models retrained?