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
Tag: Large Language Models
Streaming Responses for LLMs: Implementing Server-Sent Events
Streaming LLM responses dramatically improves user experience. After implementing streaming for 20+ LLM applications, I’ve learned what works. Here’s the complete guide to implementing Server-Sent Events for LLM streaming. Figure 1: Streaming Architecture Why Streaming Matters Streaming LLM responses provides significant benefits: Perceived performance: Users see results immediately, not after 10+ seconds Better UX: Progressive… Continue reading
RESTful AI API Design: Best Practices for LLM APIs
Designing RESTful APIs for LLMs requires careful consideration. After building 30+ LLM APIs, I’ve learned what works. Here’s the complete guide to RESTful AI API design. Figure 1: RESTful AI API Architecture Why LLM APIs Are Different LLM APIs have unique requirements: Async operations: LLM inference can take seconds or minutes Streaming responses: Need to… Continue reading
Quantization Methods for LLMs: GPTQ, AWQ, and BitsAndBytes
Last year, I needed to run a 13B parameter model on a 16GB GPU. Full precision required 52GB. After testing GPTQ, AWQ, and BitsAndBytes, I reduced memory to 7GB with minimal accuracy loss. After quantizing 30+ models, I’ve learned which method works best for each scenario. Here’s the complete guide to LLM quantization. Figure 1:… 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
GraphQL for AI Services: Flexible Querying for LLM Applications
GraphQL provides flexible querying for LLM applications. After implementing GraphQL for 15+ AI services, I’ve learned what works. Here’s the complete guide to using GraphQL for AI services. Figure 1: GraphQL Architecture for AI Services Why GraphQL for AI Services GraphQL offers significant advantages for AI services: Flexible queries: Clients request exactly what they need… Continue reading
Serverless AI Architecture: Building Scalable LLM Applications
Three years ago, I built my first serverless LLM application. It failed spectacularly. Cold starts made responses take 15 seconds. Timeouts killed long-running requests. Costs spiraled out of control. After architecting 30+ serverless AI systems, I’ve learned what works. Here’s the complete guide to building scalable serverless LLM applications. Figure 1: Serverless AI Architecture Overview… 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
Cost Optimization for AI Workloads: Tracking and Reducing LLM Costs
Last quarter, our LLM costs hit $12,000. In a single month. We had no idea where the money was going. No tracking, no budgets, no alerts. That’s when I realized: cost optimization isn’t optional for AI workloads—it’s survival. Here’s how we cut costs by 65% without sacrificing quality. Figure 1: Cost Optimization Architecture The $12,000… Continue reading
Prompt Performance Monitoring: Tracking LLM Response Quality
Three weeks after launching our AI customer support system, we noticed something strange. Response quality was degrading—slowly, almost imperceptibly. Users weren’t complaining yet, but satisfaction scores were dropping. The problem? We had no way to measure prompt performance. We were optimizing blind. That’s when I built a comprehensive prompt performance monitoring system. Figure 1: Prompt… Continue reading