After 20+ years in enterprise architecture, I’ve seen that infrastructure readiness matters more than model capability for agentic AI deployment. Gartner predicts 40% of projects will be cancelled by 2027 due to infrastructure gaps, not AI failures.
Tag: AI Deployment
Production Model Deployment Patterns: From REST APIs to Kubernetes Orchestration in Python
After 20 years in this industry, I’ve seen Production Model Deployment Patterns evolve from [past state] to [current state]. The fundamentals haven’t changed, but the implementation details have. Let me share what I’ve learned. The Fundamentals Understanding the fundamentals is crucial. Many people skip this and jump to implementation, which leads to problems later. How… Continue reading
Data Pipelines for LLM Training: Building Production ETL Systems
Building production ETL pipelines for LLM training is complex. After building pipelines processing 100TB+ of data, I’ve learned what works. Here’s the complete guide to building production data pipelines for LLM training. Figure 1: LLM Training Data Pipeline Architecture Why Production ETL Matters for LLM Training LLM training requires massive amounts of clean, processed data:… Continue reading
Running LLMs on Kubernetes: Production Deployment Guide
Deploying LLMs on Kubernetes requires careful planning. After deploying 25+ LLM models on Kubernetes, I’ve learned what works. Here’s the complete guide to running LLMs on Kubernetes in production. Figure 1: Kubernetes LLM Architecture Why Kubernetes for LLMs Kubernetes offers significant advantages for LLM deployment: Scalability: Auto-scale based on demand Resource management: Efficient GPU and… Continue reading
Deploying LLM Applications on Cloud Run: A Complete Guide
Last year, I deployed our first LLM application to Cloud Run. What should have taken hours took three days. Cold starts killed our latency. Memory limits caused crashes. Timeouts broke long-running requests. After deploying 20+ LLM applications to Cloud Run, I’ve learned what works and what doesn’t. Here’s the complete guide. Figure 1: Cloud Run… Continue reading
LLM Observability: Monitoring AI Applications in Production
Last month, our LLM application started giving wrong answers. Not occasionally—systematically. The problem? We had no visibility. No logs, no metrics, no way to understand what was happening. That incident cost us a major client and taught me that observability isn’t optional for LLM applications—it’s survival. ” alt=”LLM Observability Architecture” style=”max-width: 100%; height: auto; border-radius:… Continue reading
Production RAG Architecture: Building Scalable Vector Search Systems
Three months into production, our RAG system started failing at 2AM. Not gracefully—complete outages. The problem wasn’t the models or the embeddings. It was the architecture. After rebuilding it twice, here’s what I learned about building RAG systems that actually work in production. Figure 1: Production RAG Architecture Overview The Night Everything Broke It was… Continue reading