AI Inference

AI Inference refers to the process by which an AI model makes predictions or decisions based on input data after it has been trained. This stage involves applying the learned patterns from the training phase to new, unseen data to generate outputs.

For instance, in a Machine Learning model designed for image Classification, once the model is trained on a dataset of labeled images, inference occurs when the model analyzes a new image and predicts its category (e.g., identifying a cat in a new picture).

Another example can be found in natural language processing (NLP), where a pre-trained language model generates responses to user queries. During inference, the model processes the input text and produces relevant, coherent answers.

In practical applications, inference can be seen in:

• Recommendation systems that suggest products based on user preferences.
• Fraud detection systems analyzing transactions in real-time to flag suspicious activity.
• Self-driving cars interpreting sensor data to make navigation decisions.