Organizations have traditionally spent much time collecting data, training models, and testing them, making AI application development a slow and resource-intensive process. The rise of pre-trained models has significantly accelerated this. AI applications can be prototyped much faster by leveraging pre-trained models, augmenting them with external knowledge, and building AI agents that connect to tools capable of reasoning, decision-making, and execution. Despite these advancements, developers still face challenges in building AI apps and moving them from prototype to production. As a result, only a small percentage of AI applications reach production. Overcoming these challenges is key to building scalable AI applications that deliver lasting value. This talk will explore how to take AI applications from prototype to production.
