How Leveraging Training Data with Pre-Trained Models Can Accelerate Your AI Projects
It would probably be a safe bet to assume that most of the AI you’ve interacted with was built using supervised learning. Supervised learning, which is essentially building machine learning (ML) models from scratch, has been the key driver of artificial intelligence (AI) development so far, motivated by increased access to large datasets and growth in computing power. But with many AI projects never reaching fruition due to lack of resources, one might hope there’d be a more efficient method for model creation. Fortunately, there are alternatives to supervised learning that cut down on time, money, and human effort without sacrificing quality. Leveraging your own training data for transfer learning and using pre-trained models is a machine learning technique that’s only recently starting to gain traction as technologists seek out new ways to optimize ML models. Transfer learning doesn’t require starting from scratch, and can lower the initial investment in launching AI. With transfer learning, ML becomes more widely available, enabling more companies to launch their AI projects and overall increasing the acceleration of AI adoption.