44 articles
Before you run terraform apply, wouldn't you want to know how much it'll cost? Build an AI cost estimator that reads your Terraform plan output and gives you a detailed cost breakdown using Claude as the reasoning engine.
Tired of noisy Grafana alerts that wake you up for nothing? Build an AI layer that classifies incoming alerts as actionable or noise, enriches them with context, and routes them intelligently — using Claude or GPT-4 as the reasoning engine.
Writing a Helm chart from scratch is tedious. Build a tool that takes a service description and generates a production-ready Helm chart with values.yaml, templates, and a test suite.
AWS Cost Anomaly Detection catches spikes but gives no context. Build a system that detects anomalies, uses Claude to explain what caused them, and posts actionable Slack alerts with a fix recommendation.
Stop hand-writing Kubernetes manifests from scratch. Build a tool that takes natural language descriptions and generates production-ready K8s YAML — Deployments, Services, HPA, NetworkPolicies, and more.
Feed any Dockerfile to Claude and get back a production-ready version with smaller image size, better layer caching, security fixes, and an explanation of every change.
Phi-3 Mini delivers GPT-3.5 quality at a fraction of the compute cost. Here's how to deploy it on Kubernetes using Ollama or vLLM with GPU or CPU-only nodes.
Terraform drift happens silently. Here's how to build an automated drift detector using Terraform plan + Claude API that alerts your team and explains exactly what changed.
NVIDIA NIM containers give you production-grade LLM inference with 3x better throughput than vanilla vLLM. Here's how to deploy NIM on Kubernetes with GPU nodes.
Model Context Protocol (MCP) lets AI assistants like Claude control kubectl, Terraform, and AWS CLI directly. Here's how to build your own MCP server for DevOps automation.
Tired of grepping through runbooks? Build a semantic search that finds relevant docs by meaning, not keywords — using embeddings, pgvector, and the Claude API.
Fine-tune a small LLM on domain-specific DevOps data using QLoRA, orchestrate the pipeline on Kubernetes, and serve the result with vLLM. Complete guide with code.
Writing postmortems takes 2-3 hours. Here's how to build an AI tool that generates a structured incident report from Slack logs, metrics screenshots, and alert data in minutes.
Temporal and Airflow both orchestrate workflows, but they're designed for completely different use cases. Here's the honest comparison — when to use each.
Google's Gemma 3 is open-weight and runs well on a single GPU. Here's how to deploy it on Kubernetes using vLLM, expose it as an OpenAI-compatible API, and use it in your DevOps workflows.
Ray Serve is the best way to serve ML models at scale on Kubernetes — handles batching, scaling, model composition, and GPU sharing. Complete setup guide.
Build a production MLOps pipeline on Kubernetes using MLflow for experiment tracking and model registry, and Apache Airflow for pipeline orchestration. Full setup guide.
Deploy DeepSeek R1 on your own Kubernetes cluster using Ollama or vLLM. Includes GPU node setup, Helm deployment, persistent model storage, and an OpenAI-compatible API.
Build a stateful DevOps agent using LangGraph that can plan multi-step infrastructure tasks, use tools, handle errors, and maintain conversation context — deployed on Kubernetes with a FastAPI interface.
Build a CLI tool that automatically diagnoses Kubernetes issues — OOMKilled, CrashLoopBackOff, pending pods — by gathering cluster state and asking Claude what's wrong and how to fix it.
Run LocalAI on Kubernetes to get an OpenAI-compatible API endpoint using CPU-only nodes. Deploy Llama 3, Mistral, or Phi-3 locally with no API costs, no data leaving your cluster, and full OpenAI SDK compatibility.
Use Claude or GPT-4o function calling to build a DevOps bot that can check pod status, scale deployments, query logs, and trigger pipelines — all from plain English commands in Slack or terminal.
Run Stable Diffusion (SDXL + AUTOMATIC1111) on Kubernetes with GPU node pools, autoscaling, and an API endpoint. Step-by-step guide with EKS GPU nodes, persistent model storage, and Ingress setup.
Build a Retrieval-Augmented Generation (RAG) pipeline that answers questions from your runbooks, Confluence docs, and incident history. Deploy it on Kubernetes with LlamaIndex, Ollama, and Qdrant vector database.
Deploy Langfuse on Kubernetes to get complete tracing, cost tracking, and evaluation for your LLM applications. Step-by-step guide with Helm charts, Postgres, ClickHouse, and production configuration.
Step-by-step guide to running NVIDIA Triton Inference Server on Kubernetes with GPU nodes — model repository setup, deployment, autoscaling, and monitoring.
Step-by-step guide to installing KubeFlow on Kubernetes and building your first ML pipeline — from cluster setup to a working training + serving workflow.
Step-by-step guide to deploying HuggingFace transformer models on Kubernetes using GPU nodes — from cluster setup to inference API in production.
Build a tool that converts plain English infrastructure descriptions into valid Terraform code using Claude AI — with validation, state awareness, and GitOps integration.
Build an AI-powered bot that analyzes your Kubernetes cluster, finds idle resources, oversized pods, and unused namespaces — and gives cost-cutting recommendations.
Build a DevOps AI agent that can actually run kubectl, check AWS costs, read logs, and create GitHub issues — using LangChain tool calling and Claude API.
Deploy your own ChatGPT-like interface on Kubernetes using Ollama for local LLMs and OpenWebUI for the frontend. Full setup with GPU support and persistent storage.
MLflow tracks your ML experiments, models, and metrics. Here's how to deploy a production MLflow tracking server on Kubernetes with PostgreSQL and S3 artifact storage.
Build a GitHub Actions workflow that automatically reviews every pull request using Claude AI — catches bugs, security issues, and bad patterns before human review.
Build a Slack bot that receives Kubernetes Alertmanager webhooks, calls Claude AI to explain the alert and suggest fixes, then posts actionable runbook steps in Slack.
vLLM is the fastest open-source LLM inference engine. Here's how to deploy it on Kubernetes with GPU nodes, expose an OpenAI-compatible API, and scale it.
Ollama makes running LLMs locally easy. Running it on Kubernetes makes it scalable, persistent, and accessible to your whole team or application stack. Here's the complete setup — CPU and GPU, with persistent model storage and a production-ready deployment.
Static alerts miss 40% of real incidents. Learn how AI and ML-based anomaly detection — using tools like Prometheus + ML, Dynatrace, and custom LLM runbooks — catches what thresholds can't.
How to use AI and machine learning for Kubernetes capacity planning. Covers predictive autoscaling, cost optimization, tools like StormForge and Kubecost, and building custom ML models for resource forecasting.
How to use LLMs and AI tools for intelligent log analysis in DevOps. Covers practical workflows, open-source tools, prompt engineering for logs, and building custom log analysis agents.
How teams are building Kubernetes operators powered by LLMs to auto-remediate incidents, optimize resources, and manage complex deployments — with architecture patterns and real examples.
How LLMs and AI are transforming log analysis, anomaly detection, and root cause analysis — and the tools DevOps engineers should know about in 2026.
GPU clusters, LLM serving, AI gateways — a new role is emerging at the intersection of DevOps and machine learning. Here's what it looks like and why it matters.
MLOps explained from the ground up. Learn what MLOps is, how it differs from DevOps, the tools in the MLOps stack, and how DevOps engineers can transition into AI infrastructure roles in 2026.