Transfer learning offers several advantages to creating ML applications:
Training ML models is resource-intensive. Transfer learning accelerates adaptation to new tasks by leveraging pre-existing knowledge, reducing the need for extensive datasets and computational resources.
Models developed through transfer learning exhibit increased robustness in diverse environments. They handle real-world variability and noise more effectively, delivering superior results and adapting flexibly to unpredictable conditions.
The availability of already trained and efficient models further streamlines the development process. Access to pretrained models allows developers to benefit from the knowledge encapsulated in these models, saving time and effort in creating effective solutions.