The AI Development Lifecycle refers to the structured process of creating, deploying, and maintaining artificial intelligence models. It involves key stages such as data collection, model training, testing, deployment, and ongoing optimisation. This lifecycle ensures AI systems are effective, reliable, and continuously improved for real-world applications.
The two key phases in artificial intelligence development are the Training and Inference AI stage. During training, AI models learn from large datasets to recognise patterns and make predictions. Inference is when the trained model is deployed to process new data and deliver real-time results or insights.
