This article provides a comprehensive guide to deploying AI applications from prototype to production, focusing on .NET and Azure. It covers essential MLOps and LLMOps practices such as model deployment patterns, versioning, monitoring, CI/CD pipelines with evaluation gates, and leveraging Azure AI services. The guide also details containerization with Docker, orchestration with Kubernetes, caching strategies, scaling inference, and hardware considerations like GPU vs. CPU. AI
IMPACT Provides practical guidance for engineers on operationalizing AI models in production environments, focusing on reliability and scalability.
RANK_REASON The article is a technical guide on MLOps/LLMOps practices for deploying AI applications, not a release of a new model or frontier technology.
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