inferencing
Inferencing in AI is where the trained model is used to make predictions or decisions on new, unseen data. It is the application of the learned model to real-world tasks, such as classifying images, recognizing speech, or providing recommendations[1][3][4]. Inferencing is typically much faster than training because the model's parameters are already set, and it does not require further adjustments when processing new data[3]. The inference phase is ongoing and can be resource-intensive, especially when dealing with large-scale applications like chatbots or recommendation systems that serve millions of users[1].
Compare with: training
Citations:
[1] https://research.ibm.com/blog/AI-inference-explained
[2] https://www.performance-intensive-computing.com/objectives/tech-explainer-what-is-ai-training
[4] https://www.arm.com/glossary/ai-inference
[5] https://mitsloan.mit.edu/ideas-made-to-matter/machine-learning-explained
[7] https://blogs.nvidia.com/blog/difference-deep-learning-training-inference-ai/
[8] https://www.techtarget.com/searchenterpriseai/definition/machine-learning-ML
[11] https://www.ibm.com/topics/machine-learning
[13]
https://www.youtube.com/watch?v=BsF934iA2BY [14] https://en.wikipedia.org/wiki/Machine_learning [15] https://www.backblaze.com/blog/ai-101-training-vs-inference/ [16] https://www.techtarget.com/searchenterpriseai/definition/AI-Artificial-Intelligence [17] https://www.gigabyte.com/Glossary/ai-inferencing [18] https://stagezero.ai/blog/what-is-training-data/ [19] https://www.run.ai/guides/machine-learning-inference/understanding-machine-learning-inference [20] https://www.transcribeme.com/blog/what-is-ai-training-data/