🏗️ Data and Infrastructure

People or software tools add task-relevant tags, notes or boundaries to data for model training, testing, analysis or monitoring.

Using on-demand computing services over a network, instead of relying only on machines you manage directly.

Compute is the processing capacity used to train and run AI systems, with different needs before deployment and during everyday use.

The price charged for each small piece of text a model processes or generates, usually counted separately for input and output.

The process of checking data, fixing problems, and putting it into a usable form for analysis or model training.

Data governance is the set of rules, roles and checks that control how data is collected, stored, used, shared and corrected over time.

Deployment means putting an AI system into real use so people or software can send it live inputs and receive live outputs.

Energy and water use of AI means the electricity and water consumed in building and running AI systems; emissions are a separate impact.

CPUs, GPUs and TPUs are different kinds of computer chips that run calculations, with each designed for different patterns of work, speed, cost and energy use.

Information that describes other data, such as who created it, when, where, in what format, and under what rules it can be used.

Some values that should be present in a dataset are absent, leaving blanks that can distort analysis and model behaviour.

Data that contains errors, irrelevant variation or unclear signals, making patterns harder for people or models to detect reliably.

A control that caps how many requests or how much use a service accepts within a set time, to keep systems available, fair and affordable.

Choosing a smaller set of cases from a larger group so you can measure, inspect or train on it without using every case.

The ability of a system to handle more users, data or tasks without a serious drop in speed, quality, reliability or cost control.

Information organised into predefined fields, often in rows and columns, so software can process it predictably.

Information whose main content is not organised into consistent, predefined fields, such as emails, documents, images, audio and messages, and usually needs extra processing for analysis.

The pattern of values, categories and frequencies in a dataset, including what appears often, rarely or not at all.

Rules and arrangements about where data is stored, processed and governed, and which laws and authorities apply to it.

These terms describe mismatches between development and live data, including changed inputs, labels or relationships, which can reduce a system's reliability.

Running AI inference on a device at or near the data source, instead of sending each task to a remote server, which can reduce delay and network dependence.

A network address where software sends requests to a service and receives responses according to an API's rules.

The systems and services that keep an AI model available so it can receive requests, run on computing hardware, and return outputs reliably and securely.

Artificially generated records designed to resemble data from a real population or process for uses such as testing, training or sharing.

Scaling laws describe the regular pattern that model performance often improves as data, parameters and compute increase, though gains usually slow and practical limits remain.