Red Hat SummitWhat is AI inference?
AI inference is when an AI model provides an answer based on data. What some generally call “AI” is really the success of AI inference: the final step—the “aha” moment—in a long and complex process of machine learning technology.
Training artificial intelligence (AI) models with sufficient data can help improve AI inference accuracy and speed.
For example, when an AI model is trained on data about animals—from their differences and similarities to typical health and behavior—it needs a large data set to make connections and identify patterns.
After successful training, the model can make inferences such as identifying a breed of dog, recognizing a cat’s meow, or even delivering a warning around a spooked horse. Even though it has never seen these animals outside of an abstract data set before, the extensive data it was trained on allows it to make inferences in a new environment in real time.
Our own human brain makes connections like this too. We can read about different animals from books, movies, and online resources. We can see pictures, watch videos, and listen to what these animals sound like. When we go to the zoo, we are able to make an inference (“That’s a buffalo!”). Even if we have never been to the zoo, we can identify the animal because of the research we have done. The same goes for AI models during AI inference.
Why is AI inference important?
AI inference is the operational phase of AI, where the model is able to apply what it’s learned from training to real-world situations. AI’s ability to identify patterns and reach conclusions sets it apart from other technologies. Its ability to infer can help with practical day-to-day tasks or extremely complicated computer programming.
