mlops

Guide to making AI systems observable with Grafana and Prometheus on Linux: install the stack, collect infrastructure (Node Exporter), GPU (NVIDIA DCGM), service (latency/throughput/errors), and model/version metrics, configure Prometheus scrapes, build Grafana panels with key PromQL (p95 latency, RPS, error %, GPU util), add SLO-protecting alerts, and use practical tuning and troubleshooting tips.

Pragmatic, bash-first guide to AI observability on Linux: in ~30 minutes set up Prometheus + Node Exporter for host metrics, a lightweight Python nvidia-smi GPU exporter, and app-level Prometheus metrics; then use eBPF/bpftrace one-liners for deep dives, apply ready-made PromQL for triage, and finish with fixes and next steps (Grafana, alerts, tracing) - no Kubernetes required, apt/dnf/zypper cmds included.

Practical guide for Linux engineers to run fast, private, scriptable AI with Ollama: why local models win (privacy, latency, cost), how to install and pull Llama 3, call via CLI/HTTP, and pipe into Bash for log triage, config review, and code assist; build an ops-tuned Modelfile, run air-gapped with managed storage, tune CPU/GPU and determinism, and follow a 20-minute rollout plan.

AI-IaC treats AI infrastructure as versioned, testable code, enabling teams to reproducibly spin up GPU/CPU nodes, networking, storage, and model services from a Bash prompt. The article demos a Terraform + Ansible + Docker Compose workflow to provision an AWS VM, deploy a FastAPI model API, and automate plan/apply/deploy, with tips on starting CPU-first, pinning versions, managing secrets, and adding observability and scale.

Guide to running reproducible, secure AI/ML workflows on Linux with Podman: install a rootless toolchain (Podman, Buildah, Skopeo), enable NVIDIA or AMD GPU acceleration, and build/run Jupyter notebook and batch inference containers. Covers CPU/CUDA Containerfiles, persistent caches, compose and systemd services, image sharing, and tips (SELinux, runtimes, rootless GPUs) to go GPU‑ready fast—no Docker daemon or sudo.

A bash-first playbook for AI on Kubernetes: install kubectl/Helm/jq and aliases; enable NVIDIA GPUs via container toolkit and device plugin; autoscale CPU paths, prioritize GPU inference, and pair with cluster autoscaling; ship models safely with Helm canaries, PDBs, and rollbacks; add Prometheus/Grafana + DCGM for GPU observability; enforce quotas and isolated GPU pools; and ready it for GitOps.

Practical, Bash-first Linux playbook to validate AI infrastructure: install fio, iperf3, nvme-cli, nvtop/htop, sysstat, Podman via apt/dnf/zypper, then run five repeatable checks—node inventory, NVMe throughput/IOPS (fio), east–west bandwidth (iperf3), GPU PyTorch smoke test with live monitoring, and container parity. Includes snapshot script, NVIDIA/AMD notes, and tips to catch PCIe, disk, MTU, and throttling issues across nodes.

Shipping models matters as much as training. This guide gives five Bash-first Linux projects: 1) reproducible ML pipelines with Git+DVC; 2) containerized FastAPI inference with Compose; 3) MLflow tracking/registry on Postgres+MinIO; 4) CI/CD via GitHub Actions; 5) Prometheus+Grafana monitoring—plus install steps and cloud-agnostic patterns to boost reproducibility, velocity, reliability, and cost control.

Practical, Bash-first checklist for deploying AI services on Linux: run a preflight host script, containerize a minimal FastAPI model server with Podman, enforce secure defaults (non-root, read-only FS, firewalls, resource limits), add observability via systemd + nginx, and roll out safely with canary and rollback. Paste-ready commands, SLO checks, and tips to make deploys boring and reliable.

Practical Bash-first guide to automate AI infrastructure: codify infra and config (Terraform + Ansible), pin GPU-ready containers (Podman/Docker + NVIDIA toolkit), make GPU node setup idempotent, orchestrate with Kubernetes + NVIDIA device plugin, and add Prometheus/DCGM observability and cost guardrails—boosting reproducibility, velocity, cost control, and compliance; includes installs, repo skeleton, and a 60-minute starter plan.

Build a Linux-first, bash-friendly AI CI/CD pipeline to make experiments reproducible and deployments reliable: scaffold a Makefile-driven Python project; pin dependencies; version data/models with DVC; track runs with MLflow; add pre-commit, Black/Ruff/Mypy, pytest, ShellCheck; containerize with Podman/Docker; automate train/test/build/push in GitHub Actions; plus apt/dnf/zypper installs, caching, security, and troubleshooting.

AI is now production ops: this Linux-first guide gives sysadmins copy-paste, cross-distro steps to run AI reliably; create reproducible Python venvs, containerize inference with rootless Podman, batch data with Bash+jq+GNU Parallel, keep services resilient via systemd, and manage big models with Git LFS and shared caches; improve security, performance, and cost while shipping a FastAPI demo from zero to service fast.