AI FUNDAMENTALS

Inference

AI Fundamentals

Definition

Inference is the process by which an already-trained AI model produces predictions or responses from new data. While training happens once (and is expensive), inference happens every time you use the model. The operational costs of AI depend primarily on inference, which is why optimizing inference speed and cost is crucial for SMEs.

Related terms

AI Fundamentals

Training

Training is the phase in which an AI model learns from data, adjusting its parameters to improve performance on a specific task. It can require enormous computational resources and large amounts of data. SMEs rarely train models from scratch: they use pre-trained models via fine-tuning or prompt engineering, drastically reducing costs and time.

Machine Learning

Model Serving

Model serving is the process of making an ML model available for use in production via APIs or web services. It includes load management, scalability, latency monitoring, and version management. Platforms like AWS SageMaker, Google Vertex AI, and open source solutions make deployment accessible to SMEs as well.

AI Fundamentals

Token

A token is the basic unit of text processed by language models. It can correspond to a word, part of a word, or a punctuation character. For example, 'automation' might be a single token, while 'automatization' could be split into multiple tokens. The cost of AI services is often calculated based on the number of tokens processed.

EXPLORE

More terms in AI Fundamentals

Artificial Intelligence

Artificial intelligence is the computer science discipline that develops systems capable of performing tasks that normally require human intelligence: understanding language, recognizing images, making decisions. For Italian SMEs, AI represents a concrete opportunity to automate repetitive processes and achieve measurable competitive advantages.

Machine Learning

Machine learning is a subfield of AI in which systems automatically learn from data without being explicitly programmed for every scenario. ML algorithms analyze patterns in historical data to make predictions on new data. In business practice, it is used to forecast demand, classify documents, and optimize production processes.

Deep Learning

Deep learning is an advanced machine learning technique that uses neural networks with many layers (hence 'deep') to learn complex data representations. It underpins modern speech recognition, machine translation, and text generation systems. It requires large amounts of data but produces extraordinarily accurate results.

Neural Network

An artificial neural network is a computational model inspired by the workings of the human brain, composed of nodes (neurons) organized in interconnected layers. Each connection has a weight that is adjusted during training. Neural networks underpin nearly all modern AI systems, from image recognition to text generation.

Language Model

A language model is an AI system trained on enormous amounts of text to understand and generate natural language. Large language models (LLMs) such as GPT and Claude can write texts, answer questions, translate, and reason about complex problems. For businesses, they represent versatile assistants for any text-based task.

GPT

GPT (Generative Pre-trained Transformer) is the architecture behind ChatGPT, developed by OpenAI. It is a language model pre-trained on vast text corpora that can be used to generate content, answer questions, and perform complex text tasks. GPT-4 and subsequent versions have demonstrated increasingly advanced reasoning capabilities.

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