Abstract
In the rapidly evolving landscape of Machine Learning (ML), ensuring the continued efficacy and reliability of deployed models is paramount. This paper introduces a robust framework for model monitoring, highlighting the capabilities of Grafana and Dynatrace. Grafana provides powerful visualization and alerting capabilities, while Dynatrace offers deep insights into system and application performance.By integrating these tools, organization can establish a holistic monitoring solution that tracks key performance indicators (KPIs), detects anomalies, and triggers alerts in real-time. This framework enables proactive management of model drift, data quality issues and resource utilization, thereby bolstering the trustworthiness of ML applications in production.Case studies and practical implementation guidelines illustrate the effectiveness of this approach across various industry domains. The proposed methodology represents a pivotal advancement in the field of ML model operations, facilitating enhanced decision-making, reduced downtime and heightened user satisfaction.
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