In the realm of artificial intelligence, large language models have emerged as powerful tools for various applications, from chatbots and content generation to translation and data analysis. These models have revolutionised the way we interact with technology and process vast amounts of text data. However, behind the scenes, training these models is a complex endeavour that involves a range of considerations and challenges.
In the fast-evolving landscape of artificial intelligence and machine learning, the development of robust models goes beyond the initial build phase. Continuous training, validation, and testing have emerged as essential practices to address inherent challenges that can affect the reliability and fairness of AI/ML models.