Inference vs. AI Training

Artificial intelligence (AI) is a rapidly growing field that involves the development of intelligent machines that can perform tasks that would normally require human intelligence. AI systems are typically trained using large amounts of data to learn how to perform a specific task. This process is known as AI training.

In contrast, inference is the process of using a trained AI model to make predictions or decisions based on new data. In other words, once an AI system has been trained, it can be used for inference to apply what it has learned to new situations.

One key difference between AI training and inference is the amount of computational power required. Training an AI model typically requires a large amount of processing power and can take a long time to complete. Inference, on the other hand, is generally faster and requires fewer computational resources.

Another difference between the two is the type of data used. During training, an AI system is fed large amounts of labeled data to learn from. Inference, on the other hand, involves using the trained model to make predictions or decisions based on new, unlabeled data.

In summary, AI training and inference are two distinct processes in developing and using artificial intelligence systems. While training involves teaching an AI system how to perform a specific task using large amounts of data, inference involves using the trained model to make predictions or decisions based on new data.