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 {{see also|Machine learning terms}}
 ==Introduction==
-In machine learning, [[inference]] refers to the process of using a [[trained model]] to make [[prediction]]s or decisions about new [[data]]. The trained model takes in [[input data]] and produces [[output]] predictions based on its patterns learned from [[training data]]. Inference is essential for making a [[machine learning model]] into a practical [[application]] as it enables the model to be utilized for its intended purposes such as [[classifying images]], [[creating text]], or [[making recommendations]].
+In [[machine learning]], [[inference]] is when you make [[prediction]]s or [[generate content]] by applying a [[trained model]] to [[new data]] such as [[unlabeled examples]] or [[prompts]].
-Inference can be performed in real-time, where predictions are made as new data becomes available, or batch mode, where predictions are made for a large set of data all at once. Speed and accuracy in inference are crucial factors when applying machine learning models since they directly impact their usability and usefulness in practical applications.
+==Inference Process=
+Inference in machine learning involves several steps. First, the trained model is loaded into memory and then new data is fed into it. Afterward, the model utilizes [[parameters]] and [[functions]] learned from its [[training data]] to make predictions or decisions about this new data.
+==Types of Inference=
+In machine learning, there are two main types: [[real-time inference]] and [[batch inference]].
+#Real-time inference refers to making predictions as new data is collected; this approach works best when the model must respond quickly to changes such as [[image recognition|image]] or [[speech recognition]] systems.
+#[[Batch inference]] on the other hand involves making predictions for a large [[dataset]] at once and is commonly employed when models don't need to respond in real-time like [[recommendation system]]s do.
+==Considerations for Inference=
+Speed and accuracy of inference are critical factors when using machine learning models. Speed of inference is especially crucial in real-time applications since it determines the model's capability to respond rapidly to changing data. On the other hand, accuracy inference has an impact on all applications since it determines usefulness and dependability of predictions made by the model.
+==Explain Like I'm 5 (ELI5)==
+Inference in machine learning can be likened to using a magic wand to make predictions about new things. It was trained on many things before, so now it can use what it knows to predict about unknown items. There are two methods for using the magical wand: making individual predictions or making multiple predictions simultaneously. Regardless, its accuracy must remain high so we can trust what it tells us.
 ==Explain Like I'm 5 (ELI5)==