Inference: Difference between revisions

Revision as of 17:51, 24 February 2023

See also: Machine learning terms

Introduction

In machine learning, inference is when you make predictions or generate content by applying a trained model to new data such as unlabeled examples or prompts.

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 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 systems 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 the usefulness and dependability of predictions made by the model.

Explain Like I'm 5 (ELI5)

Machine learning models use inference, or making a guess based on what you have learned from examples. Imagine having pictures of animals and wanting to guess which kind is in a new picture that hasn't been seen before; using what you learned from looking at other images, you could use what was known before to make your guess. That's similar to what a machine learning model does - except instead of using your brain, it uses math instead!

@@ Line 9: / Line 9: @@
 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.
+#[[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.
+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 the 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)==
 Machine learning models use inference, or making a guess based on what you have learned from examples. Imagine having pictures of animals and wanting to guess which kind is in a new picture that hasn't been seen before; using what you learned from looking at other images, you could use what was known before to make your guess. That's similar to what a machine learning model does - except instead of using your brain, it uses math instead!
-==Explain Like I'm 5 (ELI5)==
-Let's pretend you own a toy box with many toys inside, such as cars, dolls and stuffed animals. When you want to play with one of them, you reach inside and grab one out - much like how a machine learning model "infers" or makes an assumption.
-The machine learning model has encountered many examples of different things, just as you have in your toy box. When asked to make a prediction about something new, it reaches into its own "mind" to find the ideal toy to play with. Drawing upon all this knowledge from past examples, it makes an educated guess as to what the new thing might be.
-Much like you might guess that the new toy is a stuffed animal based on what you've seen before in your toy box, the machine learning model makes predictions based on information it's seen. Just as sometimes you might be wrong and pick out a car instead of a stuffed animal, so too can this model make mistakes and provide incorrect answers. But the more examples it sees and plays with toys more frequently, the better equipped it becomes at making accurate predictions!
 [[Category:Terms]] [[Category:Machine learning terms]]