Function1

Attendees, exhibitors & solutions

Who this event is for

Industry trends

Key trends shaping this market and its wider industry.

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Frequently asked

Glossary

1

Foundation model

A large model trained on broad data that can be adapted to many downstream tasks, such as text generation, summarisation or code assistance. At an AI trade event, foundation model providers typically sit alongside tooling vendors that fine-tune, evaluate or deploy these models.

2

Large language model (LLM)

A neural network trained on text to predict and generate language. LLMs power chat assistants, document processing, retrieval and agent workflows; exhibitors may show API access, private deployment options and domain-specific fine-tuning.

3

Inference

The process of running a trained model to produce an output. Inference cost and latency are key buying criteria for enterprise AI deployments, especially for real-time applications such as customer support, fraud checks or computer vision.

4

Fine-tuning

Further training a pre-trained model on domain-specific data so it performs better on a narrow task. Buyers often compare fine-tuning platforms, data requirements and evaluation methods when selecting AI vendors.

5

Retrieval-augmented generation (RAG)

A technique that combines a language model with an external search or database step so answers are grounded in selected documents or records. RAG is commonly used to reduce hallucination and connect AI assistants to enterprise knowledge.

6

MLOps

The practices and tooling used to deploy, monitor and govern machine learning models in production. MLOps platforms cover experiment tracking, model registries, CI/CD, drift monitoring and access control.

7

Vector database

A database optimised for storing and searching embeddings, which are numerical representations of text, images or other data. Vector databases are often used in semantic search, recommendation and RAG pipelines.

8

AI agent

A software system that uses a model to plan and take actions, often calling tools or APIs to complete multi-step tasks. Agentic AI is a fast-moving category with implications for workflow automation, security and human oversight.

9

Multimodal model

A model that can process or generate more than one data type, such as text, images, audio or video. Multimodal systems are relevant to content moderation, document understanding, media production and industrial inspection.

10

AI governance

Policies, controls and documentation used to manage AI risk, compliance and accountability. Governance offerings can include model cards, audit trails, bias testing, data lineage and alignment with emerging AI regulations.

11

Synthetic data

Artificially generated data used to train or test models when real data is scarce, sensitive or expensive to label. Synthetic data can support privacy-preserving AI development and edge-case testing.

12

Edge AI

Running AI models on local devices or gateways rather than only in the cloud. Edge AI is used where latency, bandwidth or data-residency requirements make centralised processing impractical, such as robotics, retail cameras or industrial sensors.