The term \"scalable transfer learning\" is primarily used in the fields of artificial intelligence, big data, smart data and digital transformation. <\/p>\n
Scalable transfer learning describes an innovative method in the field of machine learning. This involves transferring knowledge that an artificial intelligence (AI) has learnt from a specific task to new, similar tasks. The special thing about it: Thanks to its scalability, this technique can be extended to very large amounts of data and different applications without much additional effort.<\/p>\n
Imagine an AI learns to recognise photos of apples. Thanks to scalable transfer learning, it can not only use this knowledge for apples, but also very quickly transfer it to recognising pears, bananas or other fruits - without having to start from scratch every time. <\/p>\n
Companies in particular benefit from this because they save time and money. Instead of training a separate AI for each new task, existing knowledge can be reused. Overall, scalable transfer learning makes the introduction of smart AI solutions more efficient and flexible, especially in data-driven industries and during digital transformation.<\/p>","protected":false},"excerpt":{"rendered":"