Mlops

Large Language Models (LLMs) are moving from research labs into production systems at an incredible pace. As soon as organizations move beyond simple API calls to third‑party providers, a question appears: “How do we run LLMs ourselves, reliably, and at scale?” For many teams, the answer is: Kubernetes. This article dives into Kubernetes for LLMs—when it makes sense, how to design the architecture, common pitfalls, and concrete configuration examples. The focus is on inference (serving), with notes on fine‑tuning and training where relevant. ...

Large Language Models (LLMs) power everything from chatbots to code generators, but deploying them at scale requires more than just training—enter LLMOps. This zero-to-hero tutorial equips developers and AI engineers with the essentials to manage LLM lifecycles, from selection to monitoring, ensuring reliable, cost-effective production systems.[1][2] As an expert AI engineer and LLM infrastructure specialist, I’ll break down LLMOps step-by-step: what it is, why it matters, best practices across key areas, practical tools, pitfalls, and examples. By the end, you’ll have a blueprint for production-ready LLM pipelines. ...

Introduction vLLM has quickly become one of the most popular inference engines for serving large language models efficiently, thanks to its paged attention and strong OpenAI-compatible API. But as soon as you move beyond a single GPU or a single model server, you run into familiar infrastructure questions: How do I distribute traffic across multiple vLLM servers? How do I handle failures and keep latency predictable? How do I roll out new model versions without breaking clients? This is where the vLLM Router comes in. ...

Introduction Retrieval-Augmented Generation (RAG) pairs a retriever (to fetch relevant context) with a generator (an LLM) to produce accurate, grounded answers. This pattern reduces hallucinations, lowers inference costs by offloading knowledge into a searchable store, and makes updating knowledge as simple as adding or editing documents. In this guide, we’ll move from beginner-friendly RAG to advanced techniques, with practical code examples along the way. We’ll cover chunking, embeddings, vector stores, hybrid retrieval, reranking, query rewriting, multi-hop reasoning, GraphRAG, production considerations, and evaluation. A final resources chapter includes links to papers, libraries, and tools. ...