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DevOps for AI Docker containers and production deployment
DevOps for AI: Docker to Production

You've built an amazing AI application locally. Now you need to deploy it. Simple, right? Except your laptop has 64GB RAM, local...

DEVOPS FOR AI: DOCKER, CADDY, CI/CD
Comparing LLM models GPT-4 Claude Gemini Llama for AI development
Choosing the Right LLM: 2025 Guide

Selecting the right large language model for your production system has become increasingly complex in 2025. With dozens of proprietary and open-source...

LLM MODELS, PROVIDERS AND TRAINING
MLOps practices for deploying LLMs in production reliably
MLOps for LLMs: Ship AI Without Breaking

Shipping your first LLM feature feels magical. Shipping your tenth feels terrifying. Why? Because AI has a unique failure mode: it can...

MLOPS, EVALUATION AND OBSERVABILITY
LLM inference optimization strategies to reduce AI costs
LLM Inference: Cut AI Costs by 80%

AI costs are crushing startups. One company I talked to was spending $47,000/month on LLM API calls—more than their entire engineering payroll....

INFERENCE, SERVING AND COST CONTROL
RAG system architecture with hybrid search and vector databases for AI
Production RAG Systems with Hybrid Search

Retrieval-Augmented Generation (RAG) has become the standard architecture for LLM applications that need accurate, up-to-date information. However, naive RAG implementations often fail...

RAG, GRAPH RAG AND VECTOR DATABASES

© 2025 Amir Teymoori