Scalable Machine Learning: Techniques for Managing Data Volume and Velocity in AI Applications - Deekshith, Alladi (International Scientific Journal for Research, 2023)
Scalable machine learning focuses on developing techniques and methodologies to efficiently manage the increasing volume and velocity of data in artificial intelligence (AI) applications. As organizations accumulate vast amounts of data from various sources, traditional machine learning approaches often struggle to keep pace with data processing and analysis requirements. This necessitates the implementation of scalable architectures, distributed computing frameworks, and optimized algorithms to handle large datasets. Key strategies include data sampling, dimensionality reduction, parallel processing, and the use of cloud computing resources. This paper explores these techniques and their impact on improving the efficiency and effectiveness of AI applications in real-time decision-making and predictive analytics.