--- title: "#mlops articles | Hashnode" description: "MLOps is the process of taking an experimental Machine Learning model into a production system. Read the latest #mlops articles and tutorials on Hashnode." url: "https://hashnode.com/tag/mlops" --- Tag # #mlops 939 posts · 304 followers [Shubham Tatvamasi](https://hashnode.com/@ShubhamTatvamasi) · [2d ago](https://hashnode.com/posts/vllm-deep-dive-how-pagedattention-continuous-batching-and-the-v1-engine-serve-llms-in-production/6af1a0c4a32bf9f449f76bd3) ### [vLLM Deep Dive: How PagedAttention, Continuous Batching and the V1 Engine Serve LLMs in Production](https://blog.shubhamtatvamasi.com/vllm-deep-dive-how-pagedattention-continuous-batching-and-the-v1-engine-serve-llms-in-production?utm_source=hashnode&utm_medium=feed) Every layer above it routes, scales and governs requests. vLLM is the layer that turns GPU memory into tokens. 52 min read · [blog.shubhamtatvamasi.com](https://blog.shubhamtatvamasi.com/vllm-deep-dive-how-pagedattention-continuous-batching-and-the-v1-engine-serve-llms-in-production?utm_source=hashnode&utm_medium=feed) [3](https://hashnode.com/posts/vllm-deep-dive-how-pagedattention-continuous-batching-and-the-v1-engine-serve-llms-in-production/6af1a0c4a32bf9f449f76bd3) [Dmytro Nasyrov](https://hashnode.com/@dmytro-nasyrov) · [2d ago](https://hashnode.com/posts/self-hosted-inference-managed-api-12-month-operating-model/6af1a04ff9d26b3345b80d95) ### [When Does Self-Hosted Inference Beat a Managed API? A 12-Month Operating Model](https://pharos-production.hashnode.dev/self-hosted-inference-managed-api-12-month-operating-model?utm_source=hashnode&utm_medium=feed) A fictional workload ledger with capacity steps, usable-throughput assumptions and an unpriced-operations register. 19 min read · [pharos-production.hashnode.dev](https://pharos-production.hashnode.dev/self-hosted-inference-managed-api-12-month-operating-model?utm_source=hashnode&utm_medium=feed) [Noah Grinstein](https://hashnode.com/@noahcontrolplane) · [2d ago](https://hashnode.com/posts/why-kubernetes-is-hard-for-ai-infrastructure-teams-the-biggest-operational-pain-points-and-how-to-reduce-them/6af18a07c987948049dbd37f) ### [Why Kubernetes Is Hard for AI Infrastructure Teams: The Biggest Operational Pain Points and How to Reduce Them](https://controlplane-eng.hashnode.dev/why-kubernetes-is-hard-for-ai-infrastructure-teams-the-biggest-operational-pain-points-and-how-to-reduce-them?utm_source=hashnode&utm_medium=feed) Why Kubernetes is hard for AI infrastructure teams: GPU scheduling, drivers, model storage, and serving pain points, and how to reduce them. 19 min read · [controlplane-eng.hashnode.dev](https://controlplane-eng.hashnode.dev/why-kubernetes-is-hard-for-ai-infrastructure-teams-the-biggest-operational-pain-points-and-how-to-reduce-them?utm_source=hashnode&utm_medium=feed) [1](https://hashnode.com/posts/why-kubernetes-is-hard-for-ai-infrastructure-teams-the-biggest-operational-pain-points-and-how-to-reduce-them/6af18a07c987948049dbd37f) [Jakson Tate](https://hashnode.com/@jakson-tate) · [5d ago](https://hashnode.com/posts/deepseek-deepinfer-bare-metal-deployment/6aed825df20a479c8e9cc367) ### [DeepSeek DeepInfer: Production Inference Architecture, DSpark Optimization & Bare-Metal GPU Deployment](https://servermo.hashnode.dev/deepseek-deepinfer-bare-metal-deployment?utm_source=hashnode&utm_medium=feed) An SRE guide to DeepSeek-V4.1-Flash inference, SWA bounded replay, speculative decoding, and production serving requirements. 6 min read · [servermo.hashnode.dev](https://servermo.hashnode.dev/deepseek-deepinfer-bare-metal-deployment?utm_source=hashnode&utm_medium=feed) [Jakson Tate](https://hashnode.com/@jakson-tate) · [5d ago](https://hashnode.com/posts/megatron-deepspeed-3d-parallelism-guide/6aed987b90a4f09d1085198a) ### [Megatron-DeepSpeed 3D Parallelism: Scaling 100B+ LLM Training](https://servermo.hashnode.dev/megatron-deepspeed-3d-parallelism-guide?utm_source=hashnode&utm_medium=feed) A production-grade SRE guide to configuring TP, PP, DP, ZeRO optimizations, and NCCL RoCEv2 networks on bare-metal GPU clusters. 9 min read · [servermo.hashnode.dev](https://servermo.hashnode.dev/megatron-deepspeed-3d-parallelism-guide?utm_source=hashnode&utm_medium=feed) [Jakson Tate](https://hashnode.com/@jakson-tate) · [5d ago](https://hashnode.com/posts/deploying-tensorrt-llm-bare-metal/6aed91451054b27567c89d32) ### [TensorRT-LLM Production Deployment: FP8, NVFP4, MoE & Disaggregated Serving](https://servermo.hashnode.dev/deploying-tensorrt-llm-bare-metal?utm_source=hashnode&utm_medium=feed) An SRE blueprint for PyTorch backend selection, quantization, MoE parallelism, NIXL disaggregated serving, and bare-metal GPU infrastructure. 8 min read · [servermo.hashnode.dev](https://servermo.hashnode.dev/deploying-tensorrt-llm-bare-metal?utm_source=hashnode&utm_medium=feed) [Dmytro Nasyrov](https://hashnode.com/@dmytro-nasyrov) · [Nov 1](https://hashnode.com/posts/five-model-routing-architectures-cost-controlled-ai-systems-compared/6ae7342dc377979b095e95fc) ### [Five Model-Routing Architectures for Cost-Controlled AI Systems Compared](https://pharos-production.hashnode.dev/five-model-routing-architectures-cost-controlled-ai-systems-compared?utm_source=hashnode&utm_medium=feed) One comparison matrix and a worked budget ledger for choosing how each AI request may spend, escalate or stop. 17 min read · [pharos-production.hashnode.dev](https://pharos-production.hashnode.dev/five-model-routing-architectures-cost-controlled-ai-systems-compared?utm_source=hashnode&utm_medium=feed) [George Ezejiofor](https://hashnode.com/@georgeezejiofor) · [Oct 29](https://hashnode.com/posts/kserve-model-canary-deployment-automation/6ae3c650276c1175b0a3da04) ### [Canary Releases for an ML Model on KServe: Replayed Traffic, Sidecar Metrics and Automatic Rollback](https://georgeezejiofor.com/kserve-model-canary-deployment-automation?utm_source=hashnode&utm_medium=feed) Canary Releases for an ML Model on KServe: Replayed Traffic, Sidecar Metrics and Automatic Rollback 23 min read · [georgeezejiofor.com](https://georgeezejiofor.com/kserve-model-canary-deployment-automation?utm_source=hashnode&utm_medium=feed) [Prem Choudhary](https://hashnode.com/@prem14choudhary) · [Oct 28](https://hashnode.com/posts/argocd-arc-github-runner-on-eks-with-terraform/6ae1ff97dfd6eaee76f38453) ### [Argocd & ARC-github-runner on EKS with Terraform](https://prem14choudhary.hashnode.dev/argocd-arc-github-runner-on-eks-with-terraform?utm_source=hashnode&utm_medium=feed) Argocd & ARC-github-runner on EKS with Terraform 8 min read · [prem14choudhary.hashnode.dev](https://prem14choudhary.hashnode.dev/argocd-arc-github-runner-on-eks-with-terraform?utm_source=hashnode&utm_medium=feed) [Prem Choudhary](https://hashnode.com/@prem14choudhary) · [Oct 28](https://hashnode.com/posts/observability-stack-on-eks-via-terraform/6ae202e0a36b0d92103cb032) ### [Observability Stack on EKS via TERRAFORM....!!](https://prem14choudhary.hashnode.dev/observability-stack-on-eks-via-terraform?utm_source=hashnode&utm_medium=feed) Observability Stack on EKS via TERRAFORM 11 min read · [prem14choudhary.hashnode.dev](https://prem14choudhary.hashnode.dev/observability-stack-on-eks-via-terraform?utm_source=hashnode&utm_medium=feed)