Architecturally Significant MLOps Guidelines for ML Model Integration and Deployment: a Gray Literature Review
A new review paper published on arXiv synthesizes 25 architecturally significant guidelines for MLOps, drawing from 103 web sources to address the ad hoc nature of current practices. Complementary articles on Medium detail practical experiences in building and deploying machine learning models, covering aspects from data science and experimentation to production systems, observability, and automation using tools like MLflow, FastAPI, Docker, and Kubernetes. AI
IMPACT Provides structured guidance and practical examples for improving the integration and deployment of ML models in production environments.