A COMPREHENSIVE REVIEW OF AI'S DEPENDENCE ON DATA
Keywords:
Performance Engineering, Performance Testing, Agile Methodologies, Nonfunctional SLAs, Proactive Approach, Automation, Load Testing, Benchmarking, Continuous Monitoring, OptimizationAbstract
AI relies heavily on data to function effectively, drawing upon vast datasets to train algorithms and optimize model performance. The relationship between AI and data is multifaceted, with theoretical frameworks emphasizing the critical role of high-quality data in AI development. Insufficient or biased data can significantly impact the outcomes of AI systems, highlighting the importance of data quality assurance processes. In the context of generative AI, data science plays a pivotal role in training and validating models, shaping their ability to generate realistic outputs. The integration of AI and data analytics offers valuable insights for businesses, enabling them to make informed decisions and drive innovation. Moving forward, further research into AI's dependence on data and its implications is crucial for advancing both theoretical understanding and practical applications in various domains.
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