Executive Summary HL7 v3 was designed in the 1990s as the successor to HL7 v2, promising a rigorous, model-driven approach based on the Reference Information Model (RIM). Despite 20+ years of development and standardization, v3 never achieved widespread adoption. Understanding why v3 failed—and where it still matters—is crucial for architects navigating healthcare interoperability standards. 🏥 […]
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Azure Front Door: A Solutions Architect’s Guide to Global Load Balancing and CDN
Executive Summary In an era where milliseconds of latency can translate to millions in lost revenue, global load balancing has evolved from a nice-to-have to a critical infrastructure component. Azure Front Door represents Microsoft’s answer to the challenge of delivering applications globally with enterprise-grade security and performance. Configuration Example { “name”: “my-frontdoor”, “properties”: { “enabledState”: […]
Read more →Enterprise Machine Learning in Production: Healthcare and Financial Services Case Studies
Real-world enterprise ML implementations in healthcare diagnostics and financial fraud detection. Explore RAG and LLM integration patterns, ML maturity frameworks, and strategic recommendations for building ML-enabled organizations.
Read more →Azure Container Apps: A Solutions Architect’s Guide to Serverless Containers
Azure Container Apps represents Microsoft’s serverless container platform, offering Kubernetes-like capabilities without cluster management complexity, powered by KEDA auto-scaling and native Dapr integration. Container Apps Architecture Platform Comparison Key Features Feature Description Use Case Revisions Immutable snapshots of app version Blue-green, canary deployments Traffic Splitting Route % traffic to different revisions A/B testing, gradual rollouts […]
Read more →LLM Latency Optimization: Techniques for Sub-Second Response Times
Introduction: LLM latency is the silent killer of user experience. Even the most accurate model becomes frustrating when users wait seconds for each response. The challenge is that LLM inference is inherently slow—autoregressive generation means each token depends on all previous tokens. This guide covers practical techniques for reducing perceived and actual latency: streaming responses […]
Read more →Embedding Strategies: Model Selection, Batching, and Long Document Handling
Introduction: Embeddings are the foundation of semantic search, RAG systems, and similarity-based applications. Choosing the right embedding model and strategy significantly impacts retrieval quality, latency, and cost. Different models excel at different tasks—some optimize for semantic similarity, others for retrieval, and some for specific domains. This guide covers practical embedding strategies: model selection based on […]
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