MLOps for AI: Tracking, Synthesizing, and Monitoring Models
DOI:
https://doi.org/10.36676/jrps.v16.i2.261Keywords:
MLOps, machine learning operations, model tracking, model synthesis, model monitoring, AI lifecycle, model performance, model drift, model governance, automation, data consistency, versioning, operationalization, cross-functional collaboration, AI model management.Abstract
MLOps, or Machine Learning Operations, brings together the development and operational sides of machine learning systems to make deploying, monitoring, and managing models more efficient at scale. With AI playing a bigger role in business processes, MLOps has become essential for tracking, synthesizing, and monitoring models throughout their lifecycle. This paper dives into the key elements of MLOps, covering how to track model performance, use outputs across different applications, and continuously monitor models to ensure they stay robust and fair. It highlights the value of integrating tools and frameworks to automate workflows for developing, deploying, and running models. By doing so, teams across different roles can collaborate better, ultimately improving the efficiency of managing AI models. The paper also tackles common challenges, like model drift, data consistency, version control, and governance, while discussing best practices and new solutions to address these issues. In the end, MLOps helps keep AI models reliable, compliant, and adaptable, creating long-term value in today’s fast-changing, complex environments.
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