Introduction: Google Anthos provides a unified platform for managing applications across on-premises data centers, Google Cloud, and other cloud providers. This comprehensive guide explores Anthos’s enterprise capabilities, from GKE Enterprise and Config Management to Service Mesh and multi-cluster networking. After implementing hybrid cloud architectures for enterprises with complex compliance and data residency requirements, I’ve found […]
Read more →Author: Nithin Mohan TK
Hello Namaste! 👋🏻
I'm an accomplished Enterprise Solution Architect & Engineering Leader with 20+ years of progressive experience architecting, building, and scaling transformative enterprise solutions.
⚜️ Solution Architect / AI & Cloud 📌 Galway, Ireland
HL7 v2: The Messaging Standard That Powers Healthcare IT
Executive Summary HL7 v2.x remains the most widely deployed healthcare messaging standard globally, powering 95% of hospital interfaces despite being developed in the 1980s. This deep dive explores why HL7 v2 continues to dominate healthcare IT, how it works at a technical level, and how modern .NET developers can implement robust v2 interfaces for Irish […]
Read more →LLM Monitoring and Alerting: Building Observability for Production AI Systems
Introduction: LLM monitoring is essential for maintaining reliable, cost-effective AI applications in production. Unlike traditional software where errors are obvious, LLM failures can be subtle—degraded output quality, increased hallucinations, or slowly rising costs that go unnoticed until the monthly bill arrives. Effective monitoring tracks latency, token usage, error rates, output quality, and cost metrics in […]
Read more →Machine Learning Fundamentals: A Comprehensive Guide to Enterprise AI Foundations
Discover the foundations of machine learning from an enterprise architect’s perspective. Learn core ML concepts, the ML workflow, and practical Python implementations to kickstart your AI journey.
Read more →Structured Output from LLMs: JSON Mode, Function Calling, and Pydantic Patterns (Part 1 of 2)
Introduction: Getting reliable, structured data from LLMs is one of the most practical challenges in building AI applications. Whether you’re extracting entities from text, generating API parameters, or building data pipelines, you need JSON that actually parses and validates against your schema. This guide covers the evolution of structured output techniques—from prompt engineering hacks to […]
Read more →Context Compression Techniques: Fitting More Information into Limited Token Budgets
Introduction: Context window limits are one of the most frustrating constraints when building LLM applications. You have a 100-page document but only 8K tokens of context. You want to include conversation history but it’s eating into your prompt budget. Context compression techniques solve this by reducing the token count while preserving the information that matters. […]
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