AI models are designed to solve real-world problems and perform functions like recognizing a face, translating a sentence, or even spotting a tumor in a scan. The moment of truth when a trained model takes in fresh data and gives you a prediction or decision is called AI inference. Inference requires extensive training using data, carefully chosen to enable the model to identify patterns and make connections. 

AI inference explained 

Think of training an AI model as the operational aspect of creating an AI model. After weeks of training and fine-tuning using data, inference lets you evaluate the model’s ability to do its job. The important aspect of inference is that the model makes predictions based on unfamiliar data or input. If it’s an image model, it might decide there’s a cat in the picture. If it’s a language model, it might finish your sentence or answer your question. The result could be a number, a label, a paragraph — whatever the model was built to output. 

How does AI inference work? 

Inference uses three steps to process brand-new data into a usable output or prediction. For this example, let’s consider an AI model trained to identify flowers in photos. 

• Data input: The first step is to provide your model with new data, such as a new flower photo.  

• Execution: The model will analyze different aspects of the photo like shapes, colors, proportions etc. and compare it to the information it learned during the training phase. The model is looking for patterns that match what it already knows. 

• Output: Once the model has identified the flower featured in the picture, you will get a result. It may say, ‘This image features a sunflower.’ 

The trick is making this happen fast enough for real world applications. A chatbot can’t take ten seconds to answer a simple question. A self-driving car can’t spend a full second deciding whether the obstacle up ahead is a plastic bag or a dog. GPUs, TPUs, or edge devices are types of specialized hardware that allow AI models to generate results at speed. 

Challenges of AI inferencing 

Inference sounds simple on paper. In reality, there are plenty of hurdles to contend with. 

• Complexity: AI models designed to fulfill complex functions require more extensive data and resources for effective inference. A model can easily identify one circle among a group of squares, but it’s much harder for it to spot minute irregularities on an X-ray or CT scan. 

• Resources and cost: Complex models require specialized resources which can prove expensive. Large models can burn through compute budgets if they’re running millions of inferences a day. 

• Data quality: A model is only as good as its training data. Bad data can result in inaccurate output or even model collapse. Data pre-processing is a crucial step for AI inference. 

Inference is where an AI model proves itself. A clean, efficient inference pipeline can make using a model feel seamless to the user: you give it input, it gives you output, and it just works. However, without sound training and fine-tuning, AI inference may not meet project standards. 

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