Mon, December 16, 2024
What is inferencing and training in AI?
- In short, training builds the foundation, while inferencing brings that knowledge to life in practical, real-world applications. Inferencing is when AI puts its training into practice, tackling real-world challenges like predictions and data analysis. But first, it needs thorough preparation to ensure success.
The article from TechRadar discusses the concepts of inferencing and training in the context of artificial intelligence (AI). Training in AI involves feeding large datasets into machine learning models to enable them to learn from the data, recognize patterns, and make predictions or decisions. This process requires significant computational power and time, often utilizing specialized hardware like GPUs or TPUs. On the other hand, inferencing is the phase where the trained model uses its learned knowledge to make real-time decisions or predictions on new, unseen data. This process is generally less resource-intensive than training but still needs to be efficient, especially for applications requiring quick responses like autonomous driving or real-time analytics. The article highlights the importance of both processes in the lifecycle of AI systems, noting that while training can be done offline, inferencing often needs to happen at the edge or on-device to reduce latency and enhance privacy.
Read the Full TechRadar Article at:
[ https://www.techradar.com/pro/what-is-inferencing-and-training-in-ai ]
Read the Full TechRadar Article at:
[ https://www.techradar.com/pro/what-is-inferencing-and-training-in-ai ]
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