37 articles
Step-by-step tutorial to build an AI-powered deployment health checker using Claude API and the Kubernetes Python client. Automatically diagnose failing pods, check resource limits, and get plain-English explanations of what's wrong.
How to manage LLM context windows in production systems — token budgeting, conversation compression, RAG vs context stuffing, and real strategies for keeping your LLM application fast and cheap at scale.
Build a Python tool using Claude API + GitPython that reads git commits, categorizes them by type, and auto-generates clean Markdown release notes — runs in CI before every release.
LLMs degrade silently. Here's how to build a weekly eval pipeline using Claude Haiku as an LLM judge, SQLite for score tracking, and GitHub Actions to alert on quality drops.
Build a Python CLI tool using Claude API that analyzes Kubernetes YAML manifests before deployment — catches missing resource limits, root containers, and security issues with a go/no-go score.
Deploying LLMs without guardrails causes prompt injection and data leakage. Here's how to build a layered safety system using regex, Claude-as-judge, and NeMo Guardrails.
Cloud costs spike without warning. Build a Python bot using AWS Cost Explorer + Claude API that detects anomalies using Z-score analysis and explains the spike in plain English.
Infrastructure drift — when actual cloud state diverges from Terraform state — causes silent failures. Build a bot that detects drift daily and explains what changed using Claude AI.
vLLM is the fastest LLM inference engine but out-of-the-box settings leave performance on the table. Here's how to tune batching, quantization, and memory to maximize throughput.
Build a DevOps assistant chatbot that answers infrastructure questions, generates kubectl commands, and explains errors — deployed as a Streamlit app on Kubernetes.
Turn your static runbooks into an AI system that answers 'what do I do when X happens' with step-by-step instructions retrieved from your actual documentation.
Build a tool that scans Dockerfiles for security issues using Claude API — finds hardcoded secrets, root users, unscanned base images, and missing security best practices.
AWS Bedrock now supports Meta's Llama 3 models. Here's how to deploy, call, and optimize Llama 3 on Bedrock for production use cases without managing GPU infrastructure.
Build a CLI tool that lets you describe what you want in plain English and generates the correct kubectl command — powered by Claude API.
Running LLMs in production without observability is flying blind. Here's how to instrument your LLM calls with OpenTelemetry to track traces, costs, latency, and quality metrics.
Build an AI agent that monitors your Kubernetes cluster, detects issues, diagnoses root causes using Claude, and automatically applies safe fixes — without human intervention.
When should you fine-tune an LLM vs just prompting? How do you do it on SageMaker? This guide covers the decision framework and step-by-step fine-tuning with LoRA on AWS.
Build a tool that automatically reads CI/CD failure logs, uses LangChain + Claude to diagnose the root cause, and posts a clear explanation with fix suggestions to your PR.
LiteLLM gives you one API endpoint to route between OpenAI, Anthropic Claude, Ollama, and 100+ other LLMs. Here's how to deploy it on Kubernetes with load balancing and cost tracking.
Build a tool that takes plain English descriptions and generates production-ready Terraform modules using OpenAI's function calling API. No more starting from scratch.
LLM API bills spiral fast. Here's every technique to cut your LLM costs in production without sacrificing quality — prompt caching, request batching, model routing, and quantization.
Build a Slack bot that watches your Kubernetes cluster for errors and uses Claude AI to explain what went wrong and suggest fixes — in plain English.
Step-by-step guide to deploying Mistral 7B on AWS EC2 for production use. Covers instance selection, quantization, serving with vLLM, and cost optimization.
Vibe coding — building software by describing what you want to AI — is now mainstream. 41% of global code is AI-generated. Here's what this shift means for DevOps engineers specifically.
Everyone in the office is asking this. Here's the honest, non-hype answer — what AI is actually replacing, what it can't, and what DevOps engineers should do right now.
Automate code reviews on every PR using Claude AI via GitHub Actions. The bot reviews Dockerfile security, Terraform changes, and general code quality — and posts inline comments.
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.
How AI and LLMs are being used to analyze cloud spending, right-size resources, detect waste, and automate cost optimization across AWS, GCP, and Azure in 2026.
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.
AI agents can now plan, review, and apply Terraform changes from natural language. Here's how agentic AI is transforming infrastructure-as-code workflows.
Step-by-step guide to setting up Grafana's machine learning features for anomaly detection, predictive alerting, and intelligent noise reduction. Stop alert fatigue with AI.
60% of enterprises now use AIOps self-healing. 83% of alerts auto-resolve without humans. The era of 2 AM PagerDuty wake-ups is ending. Here's what replaces it.
The definitive comparison of AIOps tools in 2026. Datadog AI, Moogsoft, PagerDuty AIOps, BigPanda, and more — features, pricing, and which one fits your team.
The future of DevOps automation is not more bash scripts. AI agents that can reason, adapt, and self-correct are quietly making traditional scripting obsolete. Here is what that means for DevOps engineers in 2026 and beyond.
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.