EVOLUTION OF ML MODELS FOR IP VIOLATION DETECTION AND THEIR CLOUD OPTIMIZATIONS
Keywords:
Machine Learning Models, Intellectual Property Protection, Cloud Optimization, Neural Networks, Federated Learning, Large Language ModelsAbstract
This technical article examines the evolution of machine learning models in IP violation detection, tracing their progression from basic text classifiers to advanced large language models. The article explores how early approaches utilizing SVMs and word embeddings laid the foundation for more sophisticated systems, leading to the integration of computer vision capabilities and convolutional neural networks for enhanced detection accuracy. The article also investigates the revolutionary impact of large language models in providing advanced contextual understanding and multi-modal analysis capabilities. Additionally, the article delves into crucial cloud optimization strategies, including model compression, distributed processing, and caching techniques, which enable efficient deployment of these increasingly complex systems. The article concludes by examining emerging trends in federated learning, zero-shot detection, and automated model adaptation, highlighting their potential to shape the future of IP violation detection while maintaining privacy and efficiency.
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