Model-Driven AI development is an important term in the fields of Artificial Intelligence, Big Data and Smart Data, as well as Digital Transformation. Here, Artificial Intelligence (AI) and its solutions are not simply \u201etried out\u201c or just trained on the basis of large amounts of data, but rather a well-thought-out model is developed first. This model describes how the AI is intended to work later on.<\/p>\n
Unlike traditional methods where AI often learns by trial and error, model-driven AI development uses predefined structures to analyse and solve problems. This allows, for example, for precisely determining which data is important, what the processes look like, and which rules the AI must follow. This reduces risks and improves the predictability of results.<\/p>\n
A vivid example: In a factory, an AI is to be used to detect defects in products. Using model-driven AI development, a model is first created that precisely defines what defects look like and how they can be detected. The AI is then trained with real product images. This ensures that the AI works faster, more reliably, and more accurately.<\/p>","protected":false},"excerpt":{"rendered":"