Introduction: Fine-tuning transforms a general-purpose LLM into a specialized model for your specific use case. While prompt engineering works for many applications, fine-tuning offers advantages when you need consistent formatting, domain-specific knowledge, or reduced latency from shorter prompts. This guide covers practical fine-tuning: when to fine-tune versus prompt engineer, preparing training data, running fine-tuning jobs […]
Read more →GPU Resource Management in Cloud: Optimizing AI Workloads
GPU resource management is critical for cost-effective AI workloads. After managing GPU resources for 40+ AI projects, I’ve learned what works. Here’s the complete guide to optimizing GPU resources in the cloud. Figure 1: GPU Resource Management Architecture Why GPU Resource Management Matters GPU resources are expensive and limited: Cost: GPUs are the most expensive […]
Read more →AWS DevOps and Infrastructure as Code: CDK, CloudFormation, Terraform, and CI/CD (Part 6 of 6)
Infrastructure as Code (IaC) enables you to manage AWS resources through code, providing version control, repeatability, and collaboration. This guide compares AWS CDK, CloudFormation, and Terraform with production-ready examples. 📚 AWS FUNDAMENTALS SERIES – FINAL PART This is the final part of a 6-part series covering AWS Cloud Platform. Part 1: Fundamentals Part 2: Compute […]
Read more →Document Processing with LLMs: From PDFs to Structured Data (Part 1 of 2)
Introduction: Documents are everywhere—PDFs, Word files, scanned images, spreadsheets. Extracting structured information from unstructured documents is one of the most valuable LLM applications. This guide covers building document processing pipelines: extracting text from various formats, chunking strategies for long documents, processing with LLMs for extraction and summarization, and handling edge cases like tables, images, and […]
Read more →Microservices Architecture Patterns for Enterprise Applications
Microservices Architecture Overview Core Design Patterns 1. Database per Service Pattern 2. API Gateway Pattern 3. Saga Pattern (Distributed Transactions) Communication Patterns Resilience Patterns Observability Patterns Common Anti-Patterns to Avoid Migration Strategy: Monolith to Microservices Conclusion
Read more →Building AI Agents with Tool Use: From ReAct to Production Systems
Introduction: AI agents represent the next evolution beyond simple chatbots—they can reason about problems, break them into steps, use external tools, and iterate until they achieve a goal. Unlike traditional LLM applications that respond to a single prompt, agents maintain state, make decisions, and take actions in the real world. The key innovation is tool […]
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