Review Paper
Year: 2021 | Month: April-June | Volume: 6 | Issue: 2 | Pages: 368-374
DOI: https://doi.org/10.52403/ijshr.20210464
Crossing Domains: The Role of Transfer Learning in Rapid AI Prototyping and Deployment
Deekshitha Kosaraju
Independent Researcher, Texas, USA
ABSTRACT
In the changing world of technology today it's crucial for AI advancements to meet the increasing need for efficiency and top-notch performance. One major hurdle in developing and implementing AI solutions is the requirement, for sets of specialized data and computing power which often slows down progress. Conventional machine learning models usually demand starting from scratch with each project result in significant time and financial investments. Transfer learning is a game changer as it allows the sharing of expertise and pre-existing models from one area to assist with tasks in another domain seamlessly bridging the gap between them This method significantly lessens the reliance on datasets lowers computational requirements and speeds up the overall process of developing AI technologies In this piece we delve into how transfer learning is pivotal in transferring knowledge across domains especially beneficial, for quickly prototyping and deploying AI solutions In our analysis of different uses like natural language processing and medical diagnostics as well as computer vision applications we show how transfer learning boosts effectiveness in scenarios with limited data availability and speeds up the implementation of models while also allowing for better adjustments to pre trained models for particular tasks. Additionally, we explore the potential ahead for transfer learning to support the development of AI models that are easily customized for new tasks, in various fields rapidly. The growing access to trained models and the ongoing progress, in transfer learning methods are setting the stage for AI to be more user friendly and effective – enabling industries to drive innovation and roll out solutions at an unprecedented speed.
Keywords: Transfer Learning, AI Prototyping, AI Deployment, Pre-trained Models, Cross-Domain Applications, Machine Learning, Natural Language Processing, Computer Vision, Medical Imaging, Model Fine-tuning, Data Scarcity, Scalability in AI.