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We are seeing more and more organisations release their inference services. The AI landscape has become overwhelming, and it can be one thing on top of another that you need to be knowledgeable about.
There is a lot of hype around generative AI, and although it would be ideal to throw in a text prompt and have your output be perfect to the T, it’s not as easy as that. In this article, I will discuss the two processes of Generative AI: training and inference.
What is the Training Process?
Machine learning resembles the human brain. The billions of neurons in our brain move around to communicate with one another, similar to artificial neural networks (ANN) and the layers of nodes that mirror biological neurons. Earlier methods of the training phase consisted of “labeled data” which is supervised by humans, which eventually became a mundane task. Then the use of big data came to light which provides a massive amount of information in both self-supervised and semi-supervised learning using unlabelled data.
The new learning process for a machine learning model has to go through trial and error with repeated iterations to make the right predictions — known as forward propagation and backpropagation. A weighted score is associated with the data parameters as the data moves from one layer to the next and the forward propagation and backpropagation ensures that the associated weighted score is accurate and that its phase of learning the data generates an accurate output.
What is the Inference Process?
Although the training process allows you to train and test your model, the next aspect a lot of people are looking into is how machine learning models process unlabelled data. This is why Inference has become the next big thing.
The logical definition of inference is an idea or conclusion based on evidence and reasoning. This is all about making a collective decision based on knowledge. So how does this work in the machine learning aspect? The inference process will compare the parameters of the new data that has been inputted by using the information of what it already has learned during the training and testing phase. During this process, human intervention in the model’s output will be used to create labeled data, which can then be used for future training model processing. This will help model improvement by ensuring outputs are correct and enhancing the level of accuracy.
The Alliance Between Training and Inference
Going through the training and inference process over again and again is what is making artificial intelligence smarter and more effective.
The two processes are deeply interconnected with one another. The training aspect is what is behind the foundations of models to better understand the patterns, relationships, and context within the data that can be later used. The inference aspect is putting the training phase into action and applying this learned knowledge to real-world scenarios which people like us will be using every day.
Without training there would be no inference and without inference, there would be no feedback to provide to the training phase. You cannot understand one without the other.
Nisha Arya is a data scientist, freelance technical writer, and an editor and community manager for KDnuggets. She is particularly interested in providing data science career advice or tutorials and theory-based knowledge around data science. Nisha covers a wide range of topics and wishes to explore the different ways artificial intelligence can benefit the longevity of human life. A keen learner, Nisha seeks to broaden her tech knowledge and writing skills, while helping guide others.