🛠 MLOps projects often run on ad hoc instinct. This study analyzes 103 practitioner blogs and whitepapers and distills 25 architecturally significant guidelines for ML model integration and deployment. Title: Architecturally Significant MLOps Guidelines for ML Model Integration and Deployment: a Gray Literature Review URL: 📝 Overview Using a gray literature review (analyzing practitioner web sources like blogs, whitepapers, and vendor docs rather than peer-reviewed papers), this paper organizes architectural guidance for integrating and deploying ML models. ❓ Challenges Solved Even as MLOps adoption grows, there's little consolidation of architectural knowledge as reusable design decisions, so teams default to ad hoc approaches and struggle to transfer experience across projects. 💡 Methodology & Proposed Approach ・33 Google queries returned 331 sources, narrowed by criteria to 103 for analysis ・Two researchers independently extracted text, resolving disagreements in consensus meetings ・Three authors synthesized practices into guidelines and used card sorting to form five categories ・The themes are CI/CD and automation, deployment strategies and environments, design and integration strategies, model serving and inference, and ML component management 🎯 Use Cases It serves as a consolidated reference for architectural decisions in ML integration and deployment, and as building blocks toward a holistic MLOps reference architecture. 📊 Results ・It extracted 25 architecturally significant guidelines, with 72% (18) mentioned four or more times, indicating practitioner consensus ・The most-cited was containerization (27 sources), followed by establishing CI/CD pipelines (53 mentions) ・Deployment had 16 guidelines versus 9 for integration, exposing an under-documented gap on the integration side #MLOps# #MachineLearning#

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