Docker
AI Infrastructure
A method of training AI models across multiple machines or processors simultaneously to handle large models or datasets more efficiently.
Detailed Explanation
Distributed Training is a method that enables AI models to be trained across multiple machines or processors simultaneously. By splitting datasets and model computations, it accelerates training times and handles larger models that require extensive computational resources. This approach improves efficiency, reduces training duration, and facilitates scalability in developing complex AI systems.
Use Cases
- •Use case: Accelerate large model training by distributing computations across multiple servers, reducing time and increasing scalability for complex AI applications.
Related Terms
Other terms in the AI Infrastructure category