156 articles
Sending every log line or metric anomaly to a cloud LLM API is expensive and adds latency. Small, fine-tuned language models running directly on edge nodes and CI runners are becoming the pattern for high-volume, low-latency DevOps automation in 2026.
Use LangGraph to build multi-agent DevOps systems where specialized Claude agents handle monitoring, incident response, and infrastructure changes — with state machines, agent handoffs, and human-in-the-loop checkpoints.
Stream Claude API responses in production using FastAPI Server-Sent Events (SSE). Covers token-by-token streaming, connection management, error handling mid-stream, and integrating with React frontends.
Handle Anthropic API rate limits, 529 overload errors, and retries correctly in production. Covers exponential backoff, token bucket rate limiting, queue-based throttling, and monitoring rate limit health.
Use Claude API tool use (function calling) to build DevOps automation that intelligently calls kubectl, AWS CLI, and monitoring APIs — with parallel tool execution, error handling, and real production patterns.
Running out of context window in production LLM apps? This guide covers summarization pipelines, sliding window patterns, RAG for infinite context — with Python code for each strategy.
Process thousands of LLM requests at 50% lower cost using Anthropic's Message Batches API. Complete guide with Python implementation, error handling, polling patterns, and production use cases for DevOps automation.
Choosing a vector database for your LLM application? This guide covers production patterns for Pinecone, pgvector, and Chroma — embedding strategies, index tuning, chunking, and when each database is the right choice.
Stop parsing JSON manually from LLM responses. Use Pydantic models with Claude API to get validated, typed structured outputs — with retry logic, partial failure handling, and real production patterns.
Build a multi-step AI agent using Claude API and LangGraph that analyzes your AWS costs, identifies waste, and autonomously applies rightsizing recommendations — cutting cloud bills by 20-40% with minimal human involvement.
Most LLM agent tutorials show toy examples. This post covers what production LLM agents actually need — persistent memory across sessions, reliable tool execution, structured planning loops, error recovery, and observability — with working code using Claude API.
AI agents that detect incidents, diagnose root causes, execute remediation, and write postmortems without human intervention are already running in production. Here is what agentic DevOps looks like and where it is heading.
Build a production-ready log pattern classifier using Claude API that automatically categorizes log lines into errors, warnings, anomalies, and noise — saving on-call engineers hours of manual log triage.
Tutorial to build a tool that analyzes Dockerfiles for security vulnerabilities, bad practices, and layer optimization issues — using Claude API to generate specific, actionable fixes with a corrected Dockerfile.
How to add OpenTelemetry tracing to LLM applications in production — instrumenting Anthropic SDK calls, tracking token usage and latency, connecting LLM traces to your existing observability stack.
Step-by-step tutorial to build an AI incident commander that takes an alert, gathers context from Kubernetes and AWS, generates a structured runbook, and coordinates the incident response — using Claude API with tool use.
How to implement Retrieval Augmented Generation (RAG) for DevOps runbooks and incident knowledge — using ChromaDB as the vector store and Anthropic Claude for answer generation, with real production patterns.
Step-by-step tutorial to build a tool that takes any Kubernetes YAML manifest, explains what it does in plain English, catches misconfigurations and security issues, and suggests improvements — using Claude API.
How to implement real-time streaming LLM responses using FastAPI Server-Sent Events (SSE) and the Anthropic SDK — with proper error handling, client reconnection, and production deployment patterns.
How to build LLM agents that use tools to automate DevOps tasks — querying Kubernetes, running Terraform, checking AWS resources — using Claude API's tool use feature with real production patterns.
Step-by-step tutorial to build a tool that automatically generates operational runbooks for any Kubernetes resource using Claude API — with real examples for Deployments, StatefulSets, and CronJobs.
How to use Claude API's prompt caching feature to dramatically reduce costs and latency for LLM applications with repeated context — system prompts, documents, conversation history, and RAG results.
How to evaluate LLM output quality in production using LLM-as-Judge — building automated evaluation pipelines, scoring rubrics, and golden dataset testing with Claude API. With real code examples.
Step-by-step tutorial to build an AI-powered AWS cost anomaly detector using Claude API and AWS Cost Explorer. Automatically identify unusual spending patterns, find the responsible service, and get plain-English explanations with fix recommendations.
How to reliably get structured JSON output from Claude API in production — using tool use, Pydantic validation, retry logic, and schema design patterns that prevent the most common failures.
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 RAG-based chatbot with Claude API that answers new engineer questions from your runbooks and docs. Full Python FastAPI code, cosine similarity retrieval, and Slack bot deployment.
How to detect and mask PII before it reaches your LLM and leaks in responses. Covers Microsoft Presidio, regex detection for Indian data (Aadhaar, PAN), token-based masking, and audit logging.
Build a GitHub Action that automatically generates detailed PR descriptions using Claude API. Reads the git diff, generates summary, testing instructions, and risk assessment, then posts it back to the PR.
Real attack patterns on LLM applications and how to defend against them. Covers direct prompt injection, indirect injection via RAG documents, context poisoning, and Python code for secure vs vulnerable patterns.
Build a Python script that collects pending PRs, firing Prometheus alerts, Kubernetes warnings, and failed CI jobs, then uses Claude API to generate a prioritized morning briefing posted to Slack.
Build production-grade LLM error handling in Python. Covers exponential backoff, fallback chains, circuit breaker pattern, timeout budgets, and dead letter queues using tenacity.
Build a Python tool that collects kubectl events and pod status, sends them to Claude API for AI analysis, identifies root causes, and posts alerts to Slack — deployable as a Kubernetes CronJob.
How to control LLM costs at scale — token counting, prompt compression, semantic caching with Redis, tiered model routing, and cost attribution dashboards. Python code included.
Build a Python Slack bot that reads incident messages, classifies them by severity (P1/P2/P3), affected service, and type using Claude Haiku, then posts structured summaries to a dedicated channel.
Build a model router in Python that picks cheap vs expensive LLMs based on query complexity. Covers cost-based routing, latency fallbacks, LiteLLM router, and tracking routing decisions with the Anthropic SDK.
Build a Python tool using PyGithub and the Anthropic Claude API to detect flaky tests in GitHub Actions, analyze root causes with AI, and generate fix reports — runs as a weekly cron job.
How to enforce structured JSON output from LLMs in production — Claude tool use, OpenAI JSON mode, Pydantic + Instructor validation, retry logic, schema versioning, and testing pipelines with the Anthropic SDK.
Use Python, boto3, and the Claude API to automatically audit your AWS environment for security misconfigurations and get AI-powered remediation recommendations.
How to red-team your LLM application before shipping to production. Covers prompt injection, jailbreaks, PII leakage, automated adversarial testing with Python, NeMo Guardrails defense, and building a repeatable test suite.
Build a Python script that reads kubectl top output and current resource requests/limits, sends it to Claude API (claude-haiku-4-5), and gets back specific CPU/memory rightsizing recommendations to cut cloud costs by 30-40%.
Build a production-ready multi-agent system with LangGraph for DevOps automation — Planner, Executor, and Reviewer agents with shared state, conditional edges, human-in-the-loop checkpoints, and LangSmith observability.
Use LangChain and Claude API to detect drift between your Terraform state and actual AWS/GCP/Azure resources, then generate a plain-English remediation report.
Running large-scale data processing in 2026? Compare Databricks, AWS EMR, and self-managed Spark on Kubernetes — cost, complexity, and when each makes sense.
Stateless LLMs forget everything between turns. Here's how to implement persistent conversation memory using Redis for short-term and vector databases for long-term memory.
Automatically detect unusual cloud cost spikes, identify the cause, and get an AI-generated explanation and recommended fix using Claude API and Prometheus metrics.
Compare the top AI-assisted Kubernetes debugging tools. K8sGPT, kubectl-ai, and k9s each help differently — here's when to use which and what they actually do.
When you have thousands of LLM requests to process, synchronous API calls don't scale. Here's how to build async batch inference pipelines with Celery, Redis, and Anthropic.
Use the Model Context Protocol (MCP) with Claude API to build a DevOps assistant that can read Kubernetes state, run Terraform commands, and query metrics via natural language.
Tool calling lets LLMs execute real functions. Parallel tool calling makes agents fast. Here's how to implement it properly with Claude API and handle errors gracefully.
Use LangGraph to build an agentic SRE assistant that reads Kubernetes state, queries Prometheus, and executes runbook steps autonomously using Claude API.
When your documents exceed the context window, you need chunking, summarization, and retrieval strategies. Here's how to handle long context in production LLM apps.
Automatically label, prioritize, and route GitHub issues using Claude API. Save your team hours of manual triage every week with this Python bot.
Serving vision-enabled LLMs (Claude Vision, GPT-4V) in production requires different patterns from text-only models. Here's how to handle images, latency, and cost at scale.
Automatically review terraform plan output with Claude API to catch risky changes, unintended destroys, and security issues before they hit production.
How to safely test new LLM models and prompts in production using A/B testing, shadow mode, and traffic splitting — without risking user experience.
A 200-line Kubernetes manifest diff in a PR review is easy to skim past and miss what actually changed. Build a tool that uses Claude API to explain YAML diffs in plain English before anyone approves the PR.
Automated evals catch some quality regressions, but real user feedback catches what your test set never anticipated. Here's how to build a feedback loop that actually improves prompts and routing over time.
Helm's schema validation catches type errors but misses logical mistakes — a memory limit lower than the request, a missing resource block, an image tag that's 'latest' in production. Build a smarter validator with Claude API.
One Anthropic API key, ten teams using it, one bill at the end of the month with no idea who spent what. Here's how to attribute LLM costs per team, per feature, and per customer so finance can actually chargeback.
Reactive autoscaling fixes problems after they happen. Build a forecasting tool using Facebook's Prophet library on historical Prometheus metrics to predict capacity needs days ahead — before traffic spikes hit.
A prompt change that seemed like an improvement quietly breaks output quality for a subset of users. Here's how to version prompts like code, test changes before shipping, and roll back fast when something goes wrong.
Writing NetworkPolicy YAML by hand is error-prone and easy to get wrong. Build a tool that reads your namespace's actual traffic patterns and generates a least-privilege NetworkPolicy using Claude API.
Exact-match caching misses most repeat LLM queries because users phrase things differently. Semantic caching with embeddings + Redis catches near-duplicate questions and can cut your LLM API bill significantly.
Your logs have the answers — but there are too many to read. Build an AI anomaly detector that queries Loki for unusual patterns and uses Claude API to explain what's wrong and what to do.
LLMs return unpredictable text. Instructor + Pydantic turns that into validated, typed Python objects — automatically retrying when the model returns garbage. Here's how to use it in production.
Your Kubernetes cluster is probably wasting 40-60% of its compute cost on over-provisioned resources. Build an AI-powered cost optimizer that reads Prometheus metrics and gives specific rightsizing recommendations.
Most teams ship RAG pipelines and never know if they're actually working. RAGAS gives you automated metrics — faithfulness, answer relevancy, context precision. LangSmith gives you tracing and regression testing. Here's how to wire both together.
ArgoCD tells you when drift happens — but not why it matters or what to do. Build an AI agent with LangChain that detects drift, explains the risk, and suggests the right fix.
Running one LLM provider in production is a single point of failure. Here's how to build an LLM gateway with LiteLLM that routes traffic, handles fallbacks, enforces cost limits, and gives you observability.
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.
Build a tool that takes PagerDuty incident data, Slack messages, and system metrics and generates a structured blameless postmortem — timeline, root cause, action items — in minutes instead of days.
LLMs in production face real security threats: prompt injection, jailbreaks, sensitive data leakage, and SSRF via tool calls. Learn the attacks and defenses for production AI systems.
Build a CLI tool that reviews Kubernetes YAML manifests with Claude — catching missing resource limits, security issues, hardcoded secrets, anti-patterns, and suggesting fixes before kubectl apply.
Not every LLM problem needs fine-tuning. Understand when prompt engineering is enough, when to use RAG for knowledge, and when fine-tuning actually makes sense — with real decision criteria.
Build an SLO breach predictor that reads error budget burn rate from Prometheus, uses Claude to analyze patterns, and sends Slack alerts before SLOs breach — not after.
Build production-ready LLM agents with LangGraph and Claude API — tool calling, persistent memory with Redis, multi-agent orchestration, and Kubernetes deployment patterns.
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.
Building a RAG system that actually works in production requires the right chunking strategy, embedding model, and retrieval tuning. Here's what works, what doesn't, and real configuration examples.
Build an intelligent Kubernetes admission controller that uses OPA for policy enforcement and Claude AI to explain violations, suggest fixes, and auto-generate Rego policies from plain English.
Implement LLM streaming with FastAPI and Anthropic SDK using Server-Sent Events and WebSockets. Real code with token buffering, error handling, and Kubernetes deployment.
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.
Choosing a workflow orchestrator for your ML pipelines? Argo Workflows, Prefect, and Apache Airflow each have distinct strengths. Here's which to pick for your use case.
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 an AI assistant that reads PagerDuty alerts, fetches related runbooks, and generates a first-response action plan — so your on-call engineer doesn't start from zero at 3am.
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.
Build a Slack bot that uses Claude AI to explain alerts, fetch runbooks, suggest fixes, and help during incidents. Full Python project with Slack Bolt and Anthropic API.
Step-by-step guide to deploying Mistral 7B on AWS EC2 for production use. Covers instance selection, quantization, serving with vLLM, and cost optimization.
Use AI to automatically analyze your Kubernetes resource usage, detect waste, and generate optimization recommendations. Full Python project with Claude API.
Run Dify — the open-source LLM application platform — on your own Kubernetes cluster. Complete guide with Helm, persistent storage, Ingress, and connecting local models via Ollama.
Run Alibaba's Qwen2.5-Coder LLM on your own Kubernetes cluster with GPU nodes. Complete guide — from GPU node setup to serving with vLLM and integrating with VS Code via Continue.dev.
Track your error budget automatically and get AI-generated burn rate alerts and incident summaries. Build a real SLO monitoring tool with Python, Prometheus, and Claude API.
Run Code Llama on your own Kubernetes cluster with GPU nodes. Self-hosted code generation for your internal developer platform — CI pipelines, IaC generation, code review automation. Full deployment guide with vLLM and GPU support.
Manual runbooks go stale. Build a system that watches your Kubernetes cluster, detects incidents, and generates step-by-step runbooks automatically using LLMs. Full implementation with Python, kubectl, and Ollama.
Running AI/ML workloads on Kubernetes requires GPUs. The NVIDIA GPU Operator automates everything — driver installation, container toolkit, device plugin, monitoring. Here's the complete setup guide.
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.
Qdrant is the fastest open-source vector database for RAG pipelines. Here's how to deploy it on Kubernetes with persistent storage, set up collections, and connect it to LangChain or LlamaIndex.
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.