Introduction:
Machine learning has emerged as a transformative technology with applications in various domains. With the exponential growth of data and the need for real-time decision-making, web-based machine learning applications have gained significant prominence. These applications require robust and efficient computing systems to handle large-scale data processing, model training, and inference tasks. This special issue aims to explore recent advances in computing systems for web-based machine learning applications, addressing the challenges and opportunities in this rapidly evolving field.

Topics of Interest:
1. Distributed computing architectures for web-based machine learning
2. Scalable frameworks for training and deploying machine learning models on the web
3. High-performance computing for large-scale web-based machine learning applications
4. Edge computing and federated learning for web-based machine learning
5. Optimization techniques for efficient web-based machine learning algorithms
6. Hardware acceleration and specialized processors for web-based machine learning
7. Energy-efficient computing systems for web-based machine learning
8. Cloud computing platforms and services for web-based machine learning applications
9. Security and privacy in web-based machine learning systems
10. Real-time data processing and streaming algorithms for web-based machine learning

Submission Guidelines:
Researchers and practitioners are invited to submit original research articles, case studies, and reviews related to the special issue's theme. All submissions will undergo a rigorous peer-review process to ensure the quality, novelty, and relevance of the published content. Manuscripts should follow the journal's guidelines for formatting.