Companies competing in AI model orchestration
What is AI model orchestration?
AI model orchestration refers to the process of combining multiple artificial intelligence (AI) models to achieve a common goal or set of goals. It involves integrating different AI models, each with its own strengths and weaknesses, to create a cohesive and effective AI system.
Orchestration can be applied in various ways, such as:
- Ensemble learning: This involves combining the predictions of multiple models to produce a more accurate output. For example, an ensemble of neural networks can be trained to make predictions on a dataset, and the final output can be a combination of the predictions from each model.
- Hybrid models: This involves combining different types of AI models to achieve a specific goal. For example, a hybrid model might combine a rule-based system with a deep learning model to perform a complex task.
- Hierarchical systems: This involves organizing multiple AI models into a hierarchical structure, where each model has a specific role and interacts with other models in the hierarchy.
- Distributed systems: This involves distributing AI models across multiple machines or nodes, allowing them to work together to perform a task that would be too large or complex for a single model.
When built appropriately, it is possible for AI model orchestration to improve accuracy and reliability by having the outputs of AI agents checked by other AI agents.