Scalable Transfer Learning (Glossary)

The term „scalable transfer learning“ primarily comes from the fields of Artificial Intelligence, Big Data and Smart Data, as well as Digital Transformation.

Scalable transfer learning describes an innovative method within the field of machine learning. In this approach, knowledge that an artificial intelligence (AI) has learned from a specific task is transferred to new, similar tasks. What's special about it is that scalability allows this technique to be extended to very large datasets and diverse applications without much additional effort.

Imagine an AI learning to recognise photos of apples. Thanks to scalable transfer learning, it can not only use this knowledge for apples, but also very quickly apply it to recognise pears, bananas, or other fruits – without having to start from scratch every time.

Companies benefit from this above all because they save costs and time. Instead of training a separate AI for each new task, existing knowledge can be reused. Overall, scalable transfer learning makes the introduction of smarter AI solutions more efficient and flexible, especially in data-driven industries and during digital transformation.