AI Training Observability: A Comprehensive Framework for Real-Time Monitoring, Diagnosis, and Optimization
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2025
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| _version_ | 1866901802011590656 |
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| author | Revista, Zen IA, 10 |
| author_facet | Revista, Zen IA, 10 |
| contents | This paper introduces a comprehensive framework for AI training observability, addressing the critical need for real-time monitoring, diagnosis, and optimization of complex machine learning models. As AI models become increasingly sophisticated and data-intensive, the challenges of ensuring training stability, performance, and resource efficiency are significantly amplified. Our framework provides a holistic approach to observability, encompassing metrics, logs, and traces to enable fine-grained insights into the training process. We detail the architecture of the proposed framework, which includes data collection agents, a centralized monitoring platform, and automated diagnostic tools. The framework is designed to support various AI training paradigms, including supervised learning, unsupervised learning, and reinforcement learning. Furthermore, we present empirical evaluations demonstrating the effectiveness of the framework in identifying and resolving common training issues such as vanishing gradients, overfitting, and data bottlenecks. Finally, we explore advanced features for proactive optimization, including automated hyperparameter tuning and dynamic resource allocation, to accelerate the training process and improve model accuracy. Our work contributes to the growing field of AI engineering by providing practical tools and methodologies for enhancing the reliability and efficiency of AI model development. |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17824688 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | AI Training Observability: A Comprehensive Framework for Real-Time Monitoring, Diagnosis, and Optimization Revista, Zen IA, 10 This paper introduces a comprehensive framework for AI training observability, addressing the critical need for real-time monitoring, diagnosis, and optimization of complex machine learning models. As AI models become increasingly sophisticated and data-intensive, the challenges of ensuring training stability, performance, and resource efficiency are significantly amplified. Our framework provides a holistic approach to observability, encompassing metrics, logs, and traces to enable fine-grained insights into the training process. We detail the architecture of the proposed framework, which includes data collection agents, a centralized monitoring platform, and automated diagnostic tools. The framework is designed to support various AI training paradigms, including supervised learning, unsupervised learning, and reinforcement learning. Furthermore, we present empirical evaluations demonstrating the effectiveness of the framework in identifying and resolving common training issues such as vanishing gradients, overfitting, and data bottlenecks. Finally, we explore advanced features for proactive optimization, including automated hyperparameter tuning and dynamic resource allocation, to accelerate the training process and improve model accuracy. Our work contributes to the growing field of AI engineering by providing practical tools and methodologies for enhancing the reliability and efficiency of AI model development. |
| title | AI Training Observability: A Comprehensive Framework for Real-Time Monitoring, Diagnosis, and Optimization |
| url | https://doi.org/10.5281/zenodo.17824688 |