Artificial Intelligence Technology Stack

An artificial intelligence technology stack is the set of interdependent technologies required to build and operate an AI system. It extends from computing infrastructure and data at the base to models and applications at the user-facing level; labels may vary, but the functional layers remain broadly similar.

Principal layers

Each higher layer uses capabilities supplied by the layers below it.

• The compute infrastructure layer comprises processors and AI accelerators, memory, storage, networks, data centres, cloud platforms and edge hardware.
• The data layer covers data collection, storage, cleaning, labelling, integration, access control and governance so that usable datasets are available for AI.
• The model and development layer includes algorithms, machine-learning frameworks, foundation or domain-specific models, training tools, evaluation systems and machine-learning operations.
• The application layer converts model capabilities into user-facing services through application programming interfaces, software interfaces and sector-specific solutions.

How the layers work together

Data and computing resources support model development, while deployed applications use trained models to generate predictions, recommendations or content.

• During training, algorithms learn patterns from data using computing resources; during inference, a trained model processes new inputs.
• Monitoring and feedback loops help detect errors, update data or models and maintain performance after deployment.

Cross-cutting requirements and indigenisation

Cybersecurity, privacy, safety, standards, skilled personnel and energy availability affect every layer rather than forming a single separate layer.

• Domestic capability across chips, cloud infrastructure, datasets, models and applications strengthens technological autonomy and reduces concentration-related dependencies.
• Indigenisation requires an end-to-end approach, because competence only at the application layer can still leave dependence on foreign compute, platforms or models.
• Interoperable standards and responsible data governance help different layers work together while supporting accountability and wider adoption.