Artificial intelligence is about creating software programs that behave as intelligently as human beings. Deep learning and neural networks are a subset of this field. One of the fundamental AI basics you need to understand is the difference between training and inference in terms of building your AI model.
The training vs inference battle really comes down to the difference between building the model and using it to solve problems. It might seem complicated, but it is actually an easy thing to understand. As you know, the word“infer” really means to make a decision from the evidence you have gathered. After machine learning training, deployment is the next stage, and this is where inference comes in.
Introduction to AI Training
Machine learning training requires that your program use data to understand a certain concept. This deep learning uses neural networks.This means that you are essentially feeding data into your model to help it understand what the correct answer is. For example, if you needed to teach your machine learning program what a dog was, you would have to show it thousands or even millions of pictures of dogs.
This is what we call AI training. You are essentially teaching your AI model what a certain concept is. When it comes to developing machine learning applications, this is typically the first step that practitioners and data scientists have to go through. They have to find, clean, and organize the data before building features and training the model. It requires many people and a lot of computing power. However, you eventually get to a stage where the model is ready to be put into production.
How Inference Works
After building a successful model, you will eventually put it into production. You undergo the deep learning process to do this, and you are now pretty confident with the results. In terms of AI basics, the next step is inference. As the word suggests, you are going to use the model to solve problems. In fact, this was the whole idea of building a machine learning model in the first place. It means you will be feeding data into your model, but instead of training, your model will now be giving you answers based on what you have done.
In our dog example, now that your model knows what a dog looks like, you can start feeding it pictures, and it will tell you whether you have shown it a dog or not.
What That Means for Your AI Projects
The difference between inference and training is crucial because it helps you understand the point of building a machine learning model. It also helps you see how various programs work at their foundation. One of the major practices with inference is that it has now been moved to the device. For example, new iPhones have dedicated chip space for running inference programs. As devices get smarter, you will be able to run inference much faster on the device instead of relying on doing it in the cloud. It means your machine learning training and the deep learning process will be easier.