Nov 3, 2025

DevOps to AI Infrastructure Engineering — The Complete Roadmap

Nov 3, 2025

1 min read

🚀 DevOps to AI Infrastructure Engineering

🧠 From Linux → Kubernetes → GPUs → AI Platforms

A practical, infrastructure-first roadmap for DevOps, Cloud and Platform Engineers moving into AI Infrastructure, MLOps and LLMOps — without abandoning the skills that already make you valuable.

Difficulty: 🟢 Foundation → 🔴 Advanced Focus: ⚙️ Infrastructure · ☸️ Kubernetes · 🎮 GPU · 🧠 AI · 📊 Observability · 🔄 MLOps · 💰 Cost Outcome: Design, build, secure, and operate production-grade platforms for AI training and inference workloads.

🧩 Reading the roadmap: each section tells you what to learn, which tools matter, what you should be able to demonstrate, and where to continue learning. The goal is capability—not tool collection.

🧭 #	🎯 Stage	🧰 Core Tools
01	🐧 Linux & Systems	Linux · Bash · systemd
02	📦 Containers	Docker · containerd · Buildx
03	☸️ Kubernetes Platform	Kubernetes · Helm · Kustomize
04	🎮 GPU Fundamentals	NVIDIA GPUs · `nvidia-smi`
05	⚡ CUDA	CUDA Toolkit · cuDNN
06	🧩 GPU + Containers	NVIDIA Container Toolkit
07	🛠️ GPU on Kubernetes	GPU Operator · Device Plugin
08	📅 GPU Scheduling & Autoscaling	MIG · Karpenter · KEDA
09	📊 GPU Observability	DCGM · Prometheus · Grafana
10	🧠 AI Fundamentals	Transformers · LLM concepts
11	🔥 PyTorch	PyTorch · TorchServe
12	🤗 Hugging Face	Transformers · Datasets · Hub
13	🎛️ Fine-Tuning & Optimization	PEFT/LoRA · DeepSpeed · quantization
14	🚄 Inference Serving Engines	vLLM · TGI · Triton · SGLang
15	🎯 Model Serving on K8s	KServe
16	⚙️ Distributed Compute	Ray
17	🔎 Vector DBs & RAG	Milvus · Qdrant · LangChain
18	🔬 MLOps & Pipelines	MLflow · Kubeflow · Argo · DVC
19	📈 LLM Observability & Eval	Langfuse · W&B · Arize
20	🔄 GitOps / CI-CD	GitHub Actions · Argo CD · Flux
21	🏗️ Infrastructure as Code	Terraform · Ansible · Pulumi
22	🌐 AI Networking	RDMA · RoCE · InfiniBand
23	🔗 NCCL	Collective communication
24	🖥️ Slurm & HPC	Slurm · MPI
25	💾 AI Storage	Ceph · Lustre · S3
26	📦 Registries & Supply Chain	Harbor · Cosign/Sigstore
27	🔐 Security	RBAC · OPA/Gatekeeper · Falco · Vault
28	☁️ Managed Cloud AI Platforms	SageMaker · Vertex AI · Azure ML
29	💰 GPU Cost & FinOps	Kubecost · Spot/Preemptible GPUs
30	🏆 Portfolio & Career	End-to-end AI platform + positioning

🗺️ Roadmap by Phase

Phase	🎯 Mission	🧰 Main Stack	🏁 Exit Gate
🟦 Phase 1	Infrastructure Foundation	Linux · Docker · Kubernetes · Terraform · Ansible · Prometheus	Operate a production-style Kubernetes platform
🟪 Phase 2	GPU Infrastructure	NVIDIA Driver · CUDA · Container Toolkit · GPU Operator · DCGM	Provision, schedule, observe, and troubleshoot GPUs
🟩 Phase 3	AI Platform	PyTorch · Hugging Face · PEFT · vLLM · KServe · Ray	Deploy and operate real AI workloads
🟧 Phase 4	Production AI	RAG · MLflow · Argo · GitOps · Security · FinOps	Run an observable, secure, cost-aware AI platform
🟥 Phase 5	Large-Scale AI	NCCL · RDMA · InfiniBand · Slurm · Ceph/Lustre	Understand distributed multi-GPU infrastructure

🎯 Use the phases as gates, not deadlines. Move forward when you can demonstrate the exit capability—not simply when you have watched the corresponding course.

💡 The strategy: Don’t collect 50 tools. Build one coherent platform end-to-end, then progressively add capabilities. Depth around a real system beats breadth across tutorials.

AI is creating a new infrastructure layer. The industry is moving from:

Servers → VMs → Containers → Kubernetes → Cloud

toward:

Cloud / Data Center
        ↓
GPU Infrastructure
        ↓
Kubernetes / HPC
        ↓
AI Workloads
        ↓
Training + Fine-Tuning + Inference
        ↓
MLOps / LLMOps
        ↓
Production AI Platforms

This roadmap is for DevOps, Cloud and Platform Engineers who want to move into that layer by extending the skills they already have — not by starting over.

🎯 The Destination

The target is not to become a machine-learning researcher. The target is to become the engineer who can answer:

How do I provision GPU infrastructure — on-prem or in the cloud?
How do I expose GPUs safely and efficiently to containers?
How do I schedule and share GPU workloads on Kubernetes?
How do I deploy, scale, and optimize an LLM in production?
How do I fine-tune a model without an 8×A100 budget?
How do I monitor GPU utilization, cost, and inference latency?
How do I troubleshoot a failing GPU node or a hung NCCL job?
How do I automate the platform with Terraform, Ansible, and GitOps?
How do I operate distributed, multi-node, multi-GPU workloads?
How do I keep an expensive GPU cluster both utilized and secure?

The career direction:

DevOps Engineer
      │
      ▼
Cloud / Platform Engineer
      │
      ▼
AI Platform Engineer
      │
      ▼
AI Infrastructure Engineer
      │
      ├── MLOps Engineer
      ├── LLMOps / Inference Platform Engineer
      └── GPU / HPC Infrastructure Engineer

🧭 How to Use This Roadmap

Do not treat this as a checklist where every tool must be mastered equally. Use three levels of depth:

Level	Meaning
🟢 Core	Learn deeply, build with it, be able to troubleshoot it under pressure
🟡 Working knowledge	Understand it well enough to use and configure it in a project
⚪ Awareness	Understand what it’s for and when you’d reach for it; defer deep study

The objective is depth around a coherent platform, not a collection of tool logos.

🏗️ The AI Infrastructure Stack

┌─────────────────────────────────────────────────────┐
│                   AI APPLICATIONS                   │
│         (chatbots, agents, RAG pipelines, APIs)      │
├─────────────────────────────────────────────────────┤
│           RETRIEVAL / VECTOR DB / RAG LAYER          │
│      Milvus / Qdrant / Weaviate / LangChain          │
├─────────────────────────────────────────────────────┤
│         INFERENCE / MODEL SERVING ENGINES            │
│     vLLM / TGI / Triton / SGLang / KServe / Ray      │
├─────────────────────────────────────────────────────┤
│        FINE-TUNING / OPTIMIZATION LAYER              │
│  PEFT-LoRA / DeepSpeed / Quantization / Accelerate   │
├─────────────────────────────────────────────────────┤
│              MLOps / LLMOps / PIPELINES              │
│  MLflow / Kubeflow / Argo / DVC / GitOps / CI-CD     │
├─────────────────────────────────────────────────────┤
│            KUBERNETES / CONTAINERS                   │
│         Kubernetes / Helm / containerd                │
├─────────────────────────────────────────────────────┤
│               GPU ORCHESTRATION                      │
│      NVIDIA GPU Operator / MIG / Autoscaling          │
├─────────────────────────────────────────────────────┤
│               GPU SOFTWARE STACK                     │
│         Driver / CUDA / cuDNN / NCCL / DCGM           │
├─────────────────────────────────────────────────────┤
│         COMPUTE / NETWORK / STORAGE                  │
│   GPU / NVMe / RDMA / InfiniBand / Ceph / Lustre      │
├─────────────────────────────────────────────────────┤
│         SECURITY / SUPPLY CHAIN / IAM                │
│   RBAC / OPA / Falco / Cosign / Vault / Cloud IAM     │
├─────────────────────────────────────────────────────┤
│      OBSERVABILITY / COST (cross-cutting)             │
│   Prometheus / Grafana / DCGM / Kubecost / Langfuse   │
├─────────────────────────────────────────────────────┤
│        CLOUD / DATA CENTER / POWER                    │
│    Terraform / Ansible / AWS · GCP · Azure · Bare Metal│
└─────────────────────────────────────────────────────┘

You don’t need to be an expert at every layer — but you should understand how the layers interact and fail into each other.

🥇 Priority Map

🔴 P0 = must know · 🟠 P1 = strong working knowledge · 🟡 P2 = advanced specialization

🎯 How to use the priorities

🔴 P0 — Learn now: build it, break it, troubleshoot it, and be able to explain it in an interview.

🟠 P1 — Learn after the core: understand the architecture and build at least one working implementation.

🟡 P2 — Specialize when your target role requires it: these become important for large GPU clusters, HPC, distributed training, or specialized platform teams.

🚫 Do not block your progress on P2 topics. You can become productive in AI infrastructure without mastering InfiniBand, Slurm, Lustre, or NCCL on day one.

Priority	Area	Tools
🔴 P0	Linux	Linux, Bash, systemd
🔴 P0	Containers	Docker, containerd
🔴 P0	Kubernetes	Kubernetes, Helm
🔴 P0	IaC	Terraform, Ansible
🔴 P0	Observability	Prometheus, Grafana
🔴 P0	GPU fundamentals	NVIDIA GPU, `nvidia-smi`
🔴 P0	GPU containers	NVIDIA Container Toolkit
🔴 P0	Kubernetes GPU	NVIDIA GPU Operator
🔴 P0	GPU observability	DCGM, DCGM Exporter
🔴 P0	LLM serving	vLLM
🟠 P1	AI framework	PyTorch
🟠 P1	Models	Hugging Face (Transformers, Hub)
🟠 P1	Model serving	KServe, Triton Inference Server
🟠 P1	Distributed workloads	Ray
🟠 P1	GitOps	Argo CD (or Flux)
🟠 P1	ML lifecycle	MLflow
🟠 P1	Fine-tuning basics	PEFT / LoRA, quantization concepts
🟠 P1	Autoscaling GPU nodes	Karpenter / Cluster Autoscaler, KEDA
🟠 P1	Security baseline	RBAC, image scanning, Vault
🟡 P2	GPU communication	NCCL
🟡 P2	High-speed networking	RDMA, RoCE, InfiniBand
🟡 P2	HPC scheduling	Slurm
🟡 P2	AI storage	Ceph, Lustre
🟡 P2	Advanced GPU	MIG, MPS, topology-aware scheduling
🟡 P2	RAG / vector search	Milvus, Qdrant, LangChain/LlamaIndex
🟡 P2	Cost engineering	Kubecost, spot/preemptible GPU strategy
🟡 P2	Managed AI platforms	SageMaker, Vertex AI, Azure ML

🐧 01 — Linux: The Foundation

🟢 Core skill — learn deeply and prove it with a lab.

📌 Learn

Processes · Threads · CPU and memory management · Filesystems · systemd · namespaces · cgroups (v2) · Linux networking · kernel basics · performance analysis · logs · shell scripting · SSH · package management · udev/device management (needed for GPU device nodes)

🧰 Tools

ps, top / htop, vmstat, iostat, sar, ss, ip, tcpdump, strace, ltrace,
journalctl, systemctl, dmesg, lsof, nvidia-smi (preview), numactl

Why it matters

A GPU node is still a Linux server. When an AI workload fails, the root cause could be anywhere:

GPU → Driver → CUDA → Container runtime → Kubernetes → Application

…or simply:

Linux filesystem full

Infrastructure engineers need to know the difference — fast, at 2 a.m., under a page.

📚 Resources

Type	Resource
Docs	Linux Kernel Documentation
Docs	Red Hat Documentation
Course	Linux Foundation Training
Course	Linux Foundation: Introduction to Linux (LFS101)
Reference	The Linux Documentation Project (TLDP)
Certification	Linux Foundation Certified System Administrator (LFCS)

🏁 Job-Ready Checkpoint

You should be able to:

Diagnose CPU, memory, disk, process, network, and service problems from the CLI.
Explain namespaces, cgroups, systemd, logs, and Linux networking at an operational level.
Troubleshoot a production Linux host without relying on a GUI.

📦 02 — Containers

📌 Learn

OCI image spec · Dockerfiles & multi-stage builds · image layers & caching · registries · container networking · volumes · namespaces · cgroups · resource limits · GPU-enabled containers · rootless containers · image size/security hygiene

🧰 Tools

Docker / Docker Buildx
containerd + nerdctl / crictl
NVIDIA Container Toolkit (preview — full section below)
Buildah / Podman (⚪ awareness — rootless alternatives)

📚 Resources

Type	Resource
Docs	Docker Documentation
Docs	containerd Documentation
Docs	Open Container Initiative (OCI) Specs
Docs	NVIDIA Container Toolkit
Course	Docker Official Getting Started Guide

🏁 Job-Ready Checkpoint

You should be able to:

Build a small, reproducible container image.
Explain image layers, resource limits, namespaces, cgroups, networking, and volumes.
Diagnose why a container cannot access a required device or resource.

☸️ 03 — Kubernetes: Your Main Platform

Kubernetes should remain the center of the roadmap — everything else plugs into it.

📌 Learn deeply

Pods · Deployments · StatefulSets · DaemonSets · Services · Ingress/Gateway API · ConfigMaps · Secrets · resource requests/limits · scheduling · taints/tolerations · node affinity/anti-affinity · persistent storage (PV/PVC/CSI) · networking (CNI) · Helm · Kustomize · RBAC · troubleshooting · cluster upgrades · Custom Resource Definitions (CRDs) and Operators

Then layer in AI-specific capability:

Kubernetes
    ↓
GPU Nodes
    ↓
GPU Scheduling
    ↓
AI Workloads (training + inference)

🧰 Tools

Kubernetes (kubeadm, or a managed distro: EKS / GKE / AKS / OpenShift)
Helm
Kustomize
kubectl, k9s (⚪ handy TUI)

📚 Resources

Type	Resource
Docs	Kubernetes Documentation
Docs	Kubernetes Scheduling & Eviction
Docs	Helm Docs
Docs	Kustomize Docs
Interactive	Kubernetes Tutorials (Katacoda-style, official)
Certification	CKA — Certified Kubernetes Administrator
Certification	CKAD — Certified Kubernetes Application Developer

🏁 Job-Ready Checkpoint

You should be able to:

Deploy and troubleshoot workloads using kubectl.
Explain scheduling, requests/limits, taints/tolerations, affinity, Services, storage, and RBAC.
Diagnose a workload that is Pending, CrashLooping, or unable to reach another service.

🎮 04 — GPU Fundamentals

Before learning GPU tools, understand what the hardware is doing.

📌 Learn

GPU architecture (SMs, cores) · VRAM vs system RAM · HBM · CUDA cores vs Tensor Cores · PCIe generations & lanes · NVLink/NVSwitch · GPU utilization vs memory utilization · power limits · thermal limits · multi-GPU topology (nvidia-smi topo -m)

Start with:

nvidia-smi
nvidia-smi topo -m
nvidia-smi -q -d POWER,TEMPERATURE

But don’t stop at memorizing the command. Be able to explain:

Why is GPU utilization 0% while the job is “running”?
Why is GPU memory full but utilization is low?
Why is a zombie process still holding VRAM?
Why is the GPU throttling (power vs thermal vs clock)?
Why can the application see the GPU but still fail to use it?

📚 Resources

Type	Resource
Docs	NVIDIA Developer
Docs	NVIDIA Documentation Hub
Docs	nvidia-smi reference
Course	NVIDIA Deep Learning Institute (DLI)
Event	NVIDIA GTC sessions (many free on-demand)

🏁 Job-Ready Checkpoint

You should be able to:

Read nvidia-smi output and identify GPU, memory, power, temperature, and process state.
Explain why GPU utilization and GPU memory utilization can tell different stories.
Identify whether a problem is likely hardware, driver, topology, or workload related.

⚡ 05 — CUDA

You do not need to become a CUDA kernel developer initially — you need infrastructure-level fluency.

📌 Learn

CUDA runtime vs driver API · CUDA libraries (cuDNN, cuBLAS, cuFFT) · CUDA versioning & driver compatibility matrix · CUDA in containers · GPU memory allocation basics · CUDA execution model (grids/blocks/threads, conceptually)

Mental model:

Linux
  ↓
NVIDIA Driver
  ↓
CUDA
  ↓
Container Runtime
  ↓
AI Framework (PyTorch/TensorFlow)
  ↓
GPU

📚 Resources

Type	Resource
Docs	CUDA Toolkit Documentation
Docs	cuDNN Documentation
Reference	CUDA / Driver Compatibility Matrix
Course	NVIDIA DLI: Fundamentals of Accelerated Computing with CUDA

🧩 06 — NVIDIA Container Toolkit

This is the bridge between containers and GPUs.

📌 Learn

GPU runtime configuration · GPU visibility flags (NVIDIA_VISIBLE_DEVICES) · container/driver compatibility · CUDA base images · troubleshooting GPU access inside a container

Target outcome — run this and understand exactly what happens underneath:

docker run --gpus all nvidia/cuda:12.4.1-base-ubuntu22.04 nvidia-smi

📚 Resources

Type	Resource
Docs	NVIDIA Container Toolkit
Images	NVIDIA CUDA Docker Images
Docs	NVIDIA Container Toolkit — Installation Guide

🛠️ 07 — NVIDIA GPU Operator ⭐

One of the highest-leverage tools in the entire roadmap. It manages most of the NVIDIA software stack inside Kubernetes.

                 Kubernetes
                     │
             NVIDIA GPU Operator
                     │
   ┌─────────┬───────┼───────┬─────────┐
   │         │       │       │         │
 Driver  Device Plugin  DCGM  Container    MIG
         (nvidia.com/gpu)   Toolkit    Manager
   │         │       │       │         │
   └─────────┴───────┼───────┴─────────┘
                     │
                    GPU

📌 Learn

Installation (Helm chart) · configuration (ClusterPolicy CR) · driver management (pre-installed vs Operator-managed) · GPU discovery & labeling · device plugin behavior · DCGM integration · upgrades & node draining · troubleshooting (kubectl describe node, Operator pod logs)

📚 Resources

Type	Resource
Docs	NVIDIA GPU Operator
Docs	GPU Operator — Getting Started
Repo	GPU Operator GitHub
Docs	Kubernetes Device Plugin for NVIDIA GPUs

🏁 Job-Ready Checkpoint

You should be able to:

Explain what the GPU Operator manages on a Kubernetes node.
Install or upgrade the Operator using Helm.
Troubleshoot Operator pods, device discovery, drivers, and GPU scheduling.

📅 08 — GPU Scheduling & Autoscaling

A GPU cluster is only useful when workloads are scheduled efficiently and idle capacity doesn’t sit there burning money.

📌 Learn — Scheduling

GPU resource requests · node labels & selectors · affinity/anti-affinity · taints/tolerations · GPU topology awareness · GPU sharing strategies (time-slicing, MPS, MIG) · multi-GPU pod scheduling

resources:
  limits:
    nvidia.com/gpu: 1

Understand exactly what Kubernetes and the NVIDIA device plugin do with that request.

📌 Learn — Autoscaling (often missing from GPU roadmaps)

GPU nodes are the most expensive compute you’ll ever run — autoscaling them correctly is a first-class skill, not an afterthought.

Cluster/node autoscaling: Cluster Autoscaler, or Karpenter on AWS for fast, GPU-aware node provisioning
Workload autoscaling: Horizontal Pod Autoscaler (HPA) on custom metrics; KEDA for event/queue-driven scaling of inference pods
Scale-to-zero for inference: important for cost control on bursty or dev workloads

📚 Resources

Type	Resource
Docs	Kubernetes Scheduling & Eviction
Docs	NVIDIA MIG User Guide
Docs	NVIDIA Multi-Process Service (MPS)
Docs	Karpenter Documentation
Docs	Kubernetes Cluster Autoscaler
Docs	KEDA — Kubernetes Event-Driven Autoscaling

📊 09 — GPU Observability ⭐

This is an especially strong bridge from traditional DevOps into AI infrastructure.

If you already know:

Prometheus → Grafana

extend it to:

GPU → DCGM → DCGM Exporter → Prometheus → Grafana → Alertmanager

📈 Monitor

GPU utilization · GPU memory · temperature · power draw · ECC/memory errors · throttling reasons · GPU availability/health · per-workload/per-namespace GPU attribution · inference latency & throughput (from the serving layer) · queue depth

🛠️ Build

Create an actual GPU dashboard with alerting thresholds. Don’t just install Grafana and call it done — wire up alerts for “GPU idle but pod running” and “GPU memory near OOM.”

📚 Resources

Type	Resource
Docs	NVIDIA DCGM
Repo	DCGM Exporter
Docs	Prometheus Documentation
Docs	Grafana Documentation
Dashboard	NVIDIA DCGM Grafana Dashboard (community)
Docs	Prometheus Alertmanager

🏁 Job-Ready Checkpoint

You should be able to:

Trace GPU metrics from DCGM → exporter → Prometheus → Grafana.
Build useful GPU dashboards instead of merely installing monitoring components.
Investigate GPU under-utilization, memory pressure, errors, and throttling.

🧠 10 — AI Fundamentals for Infrastructure Engineers

Learn enough AI to understand what the infrastructure is actually supporting.

Understand

ML vs deep learning · neural networks (conceptually) · Transformers · LLMs · tokenization · embeddings · context windows · training vs fine-tuning vs inference · batching (static/continuous) · quantization (concept level) · model memory math (parameters × bytes-per-param × overhead)

TRAINING                         INFERENCE
────────                         ──────────
Many GPUs                        Model serving
Large datasets                   Low latency
Distributed computation          High request throughput
High network throughput          Autoscaling
Long-running jobs                GPU utilization efficiency

📚 Resources

Type	Resource
Course	Hugging Face LLM Course
Course	PyTorch Tutorials
Reading	”The Illustrated Transformer” — Jay Alammar
Course	Andrej Karpathy — Neural Networks: Zero to Hero (YouTube)
Reference	Hugging Face Model Hub

🔥 11 — PyTorch

Learn PyTorch from the infrastructure perspective, not the research perspective.

📌 Learn

Tensors · CPU vs GPU tensors · .to(device) / CUDA tensors · model definition basics · datasets & dataloaders · training loops (to read, not necessarily write from scratch) · checkpointing · mixed precision (AMP) · distributed training concepts (DDP, FSDP)

You do not need to become a full-time ML developer — you need to be able to read a training script and know what infrastructure it needs.

📚 Resources

Type	Resource
Docs	PyTorch Documentation
Course	PyTorch Tutorials
Docs	PyTorch Distributed Overview
Docs	PyTorch FSDP (Fully Sharded Data Parallel)

🤗 12 — Hugging Face

📌 Learn

Model Hub · Transformers library · tokenizers · model loading & configuration · Datasets library · pipeline() for quick inference · quantization concepts via bitsandbytes · model cards & licensing

📚 Resources

Type	Resource
Hub	Hugging Face Hub
Docs	Transformers Documentation
Docs	Hugging Face Datasets
Course	Hugging Face LLM Course
Docs	Hugging Face Accelerate

🎛️ 13 — Fine-Tuning & Model Optimization (NEW)

This layer is frequently missing from infra-focused roadmaps, but it’s exactly where DevOps/Platform skill (automation, resource management, reproducibility) matters most: making fine-tuning and optimization repeatable pipelines, not notebooks.

📌 Learn

Parameter-efficient fine-tuning (PEFT): LoRA, QLoRA, adapters — fine-tune large models on a fraction of the GPU memory
Distributed training frameworks: DeepSpeed (ZeRO stages), PyTorch FSDP, Hugging Face Accelerate
Quantization: post-training quantization concepts, bitsandbytes (8-bit/4-bit), GPTQ, AWQ, and model formats like GGUF (for CPU/edge inference via llama.cpp)
Experiment reproducibility: seeding, checkpoint management, dataset versioning (ties into MLOps, section 18)

Base Model (7B–70B params)
        ↓
  PEFT / LoRA adapter
        ↓
  DeepSpeed / FSDP (multi-GPU)
        ↓
  Quantize (GPTQ / AWQ / GGUF)
        ↓
  Serve (vLLM / TGI / llama.cpp)

📚 Resources

Type	Resource
Docs	Hugging Face PEFT
Docs	DeepSpeed Documentation
Docs	Hugging Face Accelerate
Docs	bitsandbytes Documentation
Repo	AutoGPTQ
Repo	AutoAWQ
Repo	llama.cpp / GGUF format
Guide	Hugging Face — Fine-tuning with PEFT/LoRA guide

🚄 14 — Inference Serving Engines

vLLM remains one of the best practical bridges between AI and infrastructure — but a production platform engineer should know the wider landscape and pick the right engine for the job.

vLLM ⭐ (🟢 Core)

Learn how to: load an LLM · expose an OpenAI-compatible API · use GPUs efficiently (PagedAttention) · configure continuous batching · measure throughput/latency · scale replicas · monitor serving

Client → API → vLLM → GPU → LLM

Then on Kubernetes:

Kubernetes → vLLM (Deployment) → GPU → LLM

Resource: vLLM Documentation

Other engines worth knowing (🟡 working knowledge)

Engine	Best for	Resource
Hugging Face TGI	Production HF-model serving, tight HF ecosystem integration	TGI Docs
NVIDIA Triton Inference Server	Multi-framework serving (PyTorch, TensorFlow, ONNX) at enterprise scale	Triton Docs
NVIDIA NIM	Pre-packaged, optimized inference microservices/containers	NVIDIA NIM Docs
SGLang	High-throughput serving with structured generation	SGLang Docs
Ollama	Local/dev-friendly model serving, quick prototyping	Ollama
TensorRT-LLM	Maximum NVIDIA-hardware-specific inference performance	TensorRT-LLM Docs

Rule of thumb: vLLM/TGI for most production LLM serving on GPU · Triton when you need multi-framework/multi-model serving · TensorRT-LLM/NIM when you need maximum performance on NVIDIA hardware · Ollama/llama.cpp for local dev, edge, or CPU-only.

🏁 Job-Ready Checkpoint

You should be able to:

Deploy an LLM with vLLM and expose an API.
Explain throughput, latency, batching, GPU memory pressure, and replica scaling.
Diagnose whether an inference bottleneck is model, GPU, network, storage, or application related.

🎯 15 — KServe

Once you understand Kubernetes + GPU + vLLM, learn KServe for standardized model-serving on Kubernetes.

📌 Learn

InferenceService CRD · model deployment patterns · inference endpoints · autoscaling (including scale-to-zero) · canary/blue-green rollouts · multi-model serving · integration with Triton/vLLM as the runtime

📚 Resources

Type	Resource
Docs	KServe Documentation
Repo	KServe GitHub

⚙️ 16 — Ray

Ray is essential for distributed AI workloads — training, fine-tuning, hyperparameter search, and scalable serving.

📌 Learn

Ray Core · Ray Jobs · Ray Cluster (and KubeRay operator for running Ray on Kubernetes) · Ray Serve (model serving) · Ray Train / Ray Tune · GPU workload placement · distributed execution model

Kubernetes
     ↓
  KubeRay Operator
     ↓
   Ray Cluster
  ┌──┼──┐
 GPU GPU GPU

📚 Resources

Type	Resource
Docs	Ray Documentation
Docs	KubeRay — Ray on Kubernetes
Docs	Ray Serve

🔎 17 — Vector Databases & RAG (NEW)

Most production LLM applications aren’t just “call the model” — they’re Retrieval-Augmented Generation (RAG) systems, and someone has to run the retrieval infrastructure. This is a natural extension of your database/infra background.

📌 Learn

Embeddings & similarity search (cosine/dot-product, ANN indexes like HNSW/IVF) · vector database operations (indexing, sharding, replication) · chunking strategies (conceptual) · RAG pipeline architecture · orchestration frameworks (LangChain, LlamaIndex) · caching & latency budgets for retrieval

Document → Chunk → Embed → Vector DB (index)
                                  │
User Query → Embed → Vector Search (top-k)
                                  │
                          Context + Prompt → LLM → Answer

🧰 Tools

Category	Tools
Vector databases	Milvus, Qdrant, Weaviate, pgvector (Postgres extension), Pinecone (managed)
Orchestration frameworks	LangChain, LlamaIndex
Embedding models	Hugging Face `sentence-transformers`, OpenAI/Cohere embedding APIs

📚 Resources

Type	Resource
Docs	Milvus Documentation
Docs	Qdrant Documentation
Docs	Weaviate Documentation
Docs	pgvector
Docs	Pinecone Documentation
Docs	LangChain Documentation
Docs	LlamaIndex Documentation
Docs	sentence-transformers

🔬 18 — MLOps & Pipeline Orchestration

MLOps is broader than deploying a model. Understand the full lifecycle:

Data → Experiment → Training/Fine-Tuning → Evaluation → Model Registry
   → Deployment → Monitoring → Retraining

🧰 Tools

MLflow (🟢 Core)

Experiment tracking · model registry · lifecycle management. Resource: MLflow Documentation

Argo Workflows (🟢 Core)

Workflow orchestration, Kubernetes-native jobs, multi-step pipelines. Resource: Argo Workflows

Kubeflow Pipelines (🟡 Working knowledge)

End-to-end ML pipeline orchestration purpose-built for Kubernetes; overlaps with Argo but adds ML-specific components (notebooks, katib for hyperparameter tuning). Resource: Kubeflow Documentation

DVC — Data Version Control (🟡 Working knowledge)

Git-like versioning for datasets and models; pairs naturally with your existing Git/GitOps workflow. Resource: DVC Documentation

Airflow / Prefect (⚪ Awareness)

General-purpose data/ML pipeline orchestration, common where ML sits inside a broader data-engineering org. Resources: Apache Airflow · Prefect

📈 19 — LLM Observability & Evaluation (NEW)

Traditional Prometheus/Grafana tells you if the service is healthy. It does not tell you if the model is producing good answers, drifting, hallucinating more, or costing more per request. This is a growing, infra-adjacent discipline.

📌 Learn

LLM-specific tracing (prompts, completions, token usage, cost per request) · evaluation pipelines (offline eval sets, regression testing for prompts/models) · drift and quality monitoring · human-feedback loops · guardrails/safety filtering at the infra layer

🧰 Tools

Tool	Purpose
Langfuse	LLM tracing, cost/latency analytics, prompt management (open source)
Weights & Biases (W&B)	Experiment tracking + LLM evaluation dashboards
Arize AI / Phoenix	ML/LLM observability and drift detection
Prometheus + custom exporters	Token throughput, cost-per-token, queue depth as infra metrics

📚 Resources

Type	Resource
Docs	Langfuse Documentation
Docs	Weights & Biases Documentation
Docs	Arize Phoenix (open source)

🔄 20 — GitOps / CI-CD

AI infrastructure still needs normal engineering discipline.

Git → CI (GitHub Actions) → Container/Config → Registry
    → CD (Argo CD / Flux) → Kubernetes → AI Workload

🧰 Tools

GitHub Actions (or GitLab CI / Jenkins)
Argo CD (🟢 Core) — the de facto GitOps standard in this space
Flux (⚪ Awareness) — lighter-weight GitOps alternative
Helm

📚 Resources

Type	Resource
Docs	GitHub Actions
Docs	Argo CD
Docs	Flux CD
Docs	Helm

🏁 Job-Ready Checkpoint

You should be able to:

Build an automated path from Git commit → image → registry → deployment.
Explain GitOps reconciliation and safely roll back a deployment.
Treat infrastructure and AI workloads as reproducible configuration.

🏗️ 21 — Infrastructure as Code

Keep your existing IaC skills — AI platforms need automation just as much as (arguably more than) traditional infrastructure, because GPU capacity is expensive and mistakes are costly.

🧰 Tools

Terraform (🟢 Core)
Ansible (🟢 Core)
Pulumi (⚪ Awareness — IaC in general-purpose languages)

Terraform → Cloud Infrastructure → GPU Nodes → Kubernetes
    → GPU Operator → Monitoring → AI Platform

📚 Resources

Type	Resource
Docs	Terraform Documentation
Docs	Ansible Documentation
Docs	Pulumi Documentation
Certification	HashiCorp Certified: Terraform Associate

🌐 22 — AI Networking

This is where AI infrastructure starts looking increasingly like HPC.

📌 Learn

25/40/100/200/400 GbE · RDMA · RoCE · InfiniBand · latency vs throughput · network congestion · fat-tree/topology design · east-west traffic patterns · GPU-to-GPU communication · storage-to-GPU traffic

Key idea: A large GPU cluster is also a high-performance network. Networking mistakes show up as “slow training,” not as obvious outages.

📚 Resources

Type	Resource
Docs	NVIDIA Networking
Docs	NVIDIA Networking Documentation
Docs	Linux Kernel Networking Documentation

🔗 23 — NCCL

For distributed GPU workloads, understand the NVIDIA Collective Communications Library.

GPU 1 ─────┐
GPU 2 ─────┤
GPU 3 ─────┼── NCCL ── High-speed Network
GPU 4 ─────┤
GPU 5 ─────┘

📌 Learn

Collective communication patterns · all-reduce/all-gather/broadcast · GPU-to-GPU communication (NVLink) · multi-node communication (network) · topology implications for training speed

📚 Resources

Type	Resource
Docs	NVIDIA NCCL Documentation
Repo	NCCL GitHub

🖥️ 24 — Slurm and HPC

Kubernetes dominates cloud-native AI platforms; Slurm still dominates HPC and large-scale research/training clusters. Know both.

📌 Learn

Nodes · partitions · queues · jobs · GPU allocation · sbatch, srun, squeue, sinfo · job arrays · MPI basics (as it relates to distributed training)

Slurm → Scheduler → CPU/GPU Cluster → Distributed Workloads

📚 Resources

Type	Resource
Docs	Slurm Documentation
Docs	Slurm Quick Start Guide

💾 25 — AI Storage

AI workloads can consume huge datasets — and if storage can’t feed the GPUs fast enough, you’re paying for idle compute.

Dataset → Storage → Network → GPU

Expensive GPU → idle → money burned

📌 Learn

Object storage

S3, Google Cloud Storage, Azure Blob — good for datasets/checkpoints, not for high-IOPS random access.

High-performance storage

NVMe, NFS, Ceph, Lustre, other parallel filesystems — needed when GPUs are starving for data throughput.

📚 Resources

Type	Resource
Docs	Amazon S3 Documentation
Docs	Google Cloud Storage Documentation
Docs	Ceph Documentation
Docs	Lustre Documentation

📦 26 — Container Registries & Software Supply Chain (NEW)

GPU images are large, versioned tightly to CUDA/driver combinations, and worth protecting.

📌 Learn

Private registries and replication · image signing & provenance · SBOMs (software bill of materials) · vulnerability scanning gates in CI

🧰 Tools

Tool	Purpose
Harbor	Self-hosted, security-focused container registry
Amazon ECR / GCP Artifact Registry / Azure ACR	Cloud-managed registries
Cosign (Sigstore)	Container image signing and verification
Trivy / Grype	Vulnerability scanning

📚 Resources

Type	Resource
Docs	Harbor Documentation
Docs	Sigstore / Cosign Documentation
Docs	Trivy Documentation

🔐 27 — Security

AI infrastructure does not get a free pass on security — if anything, it’s a bigger target (expensive compute, sensitive training data, exposed inference APIs).

📌 Learn

Kubernetes RBAC · Secrets management · Cloud IAM · NetworkPolicy · image scanning · container/runtime security · workload isolation · GPU isolation (MIG as a security boundary) · software supply-chain security · API security (rate limiting, auth on inference endpoints) · model security (prompt injection awareness, model/weight exfiltration risk)

🧰 Tools

Tool	Purpose
Kubernetes RBAC & NetworkPolicy	Access & traffic control
OPA / Gatekeeper	Policy-as-code admission control
Falco	Runtime threat detection
Trivy	Image/vulnerability scanning
HashiCorp Vault	Secrets management
Cloud IAM (AWS/GCP/Azure)	Identity and access at the cloud layer

📚 Resources

Type	Resource
Docs	Kubernetes Security Concepts
Docs	OPA Gatekeeper
Docs	Falco Documentation
Docs	Trivy Documentation
Docs	HashiCorp Vault Documentation
Certification	CKS — Certified Kubernetes Security Specialist

🏁 Job-Ready Checkpoint

You should be able to:

Apply least-privilege RBAC and workload isolation.
Scan and sign container images.
Protect secrets and inference endpoints.
Explain the major security boundaries of a shared AI platform.

☁️ 28 — Managed Cloud AI Platforms (NEW)

Not every organization runs bare-metal or self-managed Kubernetes for AI. Know the managed alternatives — you’ll be compared against them, and sometimes you’ll orchestrate on top of them.

Platform	Cloud	What it covers
Amazon SageMaker	AWS	Training, tuning, hosting, pipelines
Vertex AI	GCP	Training, model registry, endpoints, pipelines
Azure Machine Learning	Azure	Training, MLOps, managed endpoints

Also learn the GPU instance families on each cloud — this directly affects cost and architecture decisions:

AWS: P4/P5 (training), G5/G6 (inference), Trainium/Inferentia (custom silicon)
GCP: A2/A3 (NVIDIA A100/H100), TPU v5 (custom silicon)
Azure: ND-series, NC-series

📚 Resources

Type	Resource
Docs	Amazon SageMaker Documentation
Docs	Google Vertex AI Documentation
Docs	Azure Machine Learning Documentation
Docs	AWS EC2 GPU Instance Types

💰 29 — GPU Cost & FinOps (NEW)

Arguably the fastest way for a DevOps engineer to become indispensable on an AI team: GPUs are the single largest line item, and most ML teams have no one watching it closely.

📌 Learn

Cost attribution per namespace/team/model · spot vs on-demand vs reserved/committed-use GPU capacity · right-sizing (avoiding over-provisioned GPU memory/type) · idle-GPU detection · quantization/batching as a cost lever, not just a performance one · showback/chargeback reporting

🧰 Tools

Tool	Purpose
Kubecost	Kubernetes cost allocation and visibility, incl. GPU
Cloud-native cost tools	AWS Cost Explorer, GCP Billing Reports, Azure Cost Management
Spot/preemptible tooling	Karpenter (AWS spot-aware), GCP Preemptible/Spot VMs

📚 Resources

Type	Resource
Docs	Kubecost Documentation
Docs	AWS Cost Explorer
Docs	FinOps Foundation — Framework

🏁 Job-Ready Checkpoint

You should be able to:

Identify idle and over-provisioned GPU capacity.
Attribute GPU cost to teams, namespaces, workloads, or models.
Explain how batching, quantization, autoscaling, and spot capacity affect cost.

🧪 The Lab-First Learning Strategy

Do not spend six months watching videos. For every major technology:

Learn → Deploy → Break it → Troubleshoot it → Automate it → Document it

That last step matters most. A production engineer should be able to explain why something works, why it fails, and how to recover it — not just recite the happy path.

🏆 The Main Portfolio Project

🛠️ Build: Kubernetes AI Platform (Training-Aware, RAG-Capable, Cost-Observed)

This should be the centerpiece of the roadmap — expanded beyond “serve one model” to reflect a real platform.

                         GitHub
                            │
                            ▼
                     GitHub Actions (CI, scan, sign image)
                            │
                            ▼
                      Container Registry (Harbor/ECR)
                            │
                            ▼
                    Argo CD / Flux (GitOps)
                            │
                            ▼
                        Kubernetes
                 ┌──────────┼──────────────┐
                 │          │              │
          GPU Operator   KServe        Vector DB
                 │          │           (Qdrant/Milvus)
                 │         vLLM             │
                 │          │               │
                 └────┬─────┘               │
                      │                     │
                     GPU ── LLM ── RAG API ──┘
                      │
                Prometheus / Grafana / DCGM
                      │
                  Kubecost (cost view)
                      │
                Langfuse (LLM observability)

Automate as much as possible with: Terraform · Ansible · Helm · GitHub Actions · Argo CD.

💼 What this project proves

Capability	Evidence in the project
🐧 Linux / Systems	GPU nodes, drivers, runtime troubleshooting
📦 Containers	GPU-enabled images and registry workflow
☸️ Kubernetes	Scheduling, Services, storage, operators
🎮 GPU Infrastructure	GPU Operator, device plugin, DCGM
🧠 AI	Model serving, fine-tuning, RAG
🚄 LLMOps	vLLM, KServe, Langfuse
🔄 DevOps / GitOps	CI/CD + Argo CD + Helm
📊 Observability	Prometheus + Grafana + DCGM
🔐 Security	RBAC, image scanning/signing, policy
💰 FinOps	Kubecost + GPU utilization/cost visibility
🌐 Distributed AI	Ray, NCCL, multi-GPU concepts

🏆 Portfolio rule: document architecture decisions, failure scenarios, recovery procedures, dashboards, and cost observations. The troubleshooting story is often more valuable than the demo itself.

🧩 Project Progression (Labs)

Lab	Goal	Key tools introduced
01	Prove a container can access a GPU	NVIDIA driver, CUDA, NVIDIA Container Toolkit
02	Run a GPU workload on Kubernetes	GPU node, device plugin, resource requests, scheduling
03	Manage the GPU stack via Operator	GPU Operator, drivers, device plugin, DCGM
04	Build a real GPU dashboard	DCGM Exporter, Prometheus, Grafana, alerting
05	Serve an open LLM	vLLM, latency/throughput/GPU-utilization measurement
06	Deploy LLM serving on Kubernetes	GPU scheduling, Services, ingress, persistent model storage
07	Build a production-style AI platform	KServe, autoscaling, GitOps, CI/CD, monitoring, logging, security, IaC
08	Run a distributed GPU workload	Multiple GPUs, NCCL, Ray/KubeRay, high-speed networking concepts
09 (new)	Fine-tune a small open model with LoRA/QLoRA	PEFT, bitsandbytes, DeepSpeed/Accelerate, checkpoint versioning (DVC)
10 (new)	Build a RAG pipeline on your own infra	Vector DB (Qdrant/Milvus), LangChain/LlamaIndex, embeddings, KServe/vLLM as the generator
11 (new)	Add cost visibility and autoscaling	Kubecost, Karpenter/KEDA, spot-instance strategy, scale-to-zero

📅 Suggested 9–12 Month Roadmap

Month 1 — GPU + Containers

Linux · Docker · GPU architecture · NVIDIA drivers · CUDA · NVIDIA Container Toolkit Deliverable: GPU-enabled container lab.

Month 2 — Kubernetes GPU

Kubernetes · Helm · GPU Operator · GPU scheduling · MIG concepts Deliverable: GPU-enabled Kubernetes cluster.

Month 3 — GPU Observability

DCGM · DCGM Exporter · Prometheus · Grafana Deliverable: GPU monitoring dashboard with alerts.

Month 4 — AI Workloads

PyTorch · Hugging Face · LLM basics · vLLM Deliverable: LLM inference service.

Month 5 — AI Platform

KServe · Ray/KubeRay · Argo Workflows Deliverable: Kubernetes-based AI serving platform.

Month 6 — GitOps + Automation

Terraform · Ansible · GitHub Actions · Argo CD · Helm Deliverable: infrastructure deployed entirely from Git.

Month 7 (new) — Fine-Tuning & Optimization

PEFT/LoRA · DeepSpeed · Accelerate · quantization (GPTQ/AWQ/GGUF) Deliverable: a fine-tuned, quantized model you trained and served yourself.

Month 8 (new) — RAG & Vector Infrastructure

Qdrant/Milvus · LangChain/LlamaIndex · embeddings pipeline Deliverable: end-to-end RAG API running on your platform.

Month 9 (new) — Security, Cost & Observability Hardening

OPA/Gatekeeper · Falco · Cosign · Kubecost · Langfuse Deliverable: a platform with policy enforcement, image signing, cost dashboards, and LLM tracing.

Months 10–12 — Advanced / HPC Infrastructure

NCCL · RDMA · RoCE · InfiniBand · Slurm · Ceph/Lustre · Distributed GPU workloads Deliverable: distributed GPU/HPC lab.

🧠 What You Should Be Able to Explain

By the end of the roadmap, you should be comfortable explaining:

Infrastructure

How does a GPU node work? How does CUDA interact with the driver? How does a container access a GPU? How does Kubernetes schedule a GPU? What does GPU Operator actually install?

Performance

Why is GPU utilization low? Why is GPU memory full? Why is inference latency increasing? Why are GPUs waiting for data? What happens when the network becomes the bottleneck?

AI

Training vs inference · model memory requirements · batch size and continuous batching · quantization trade-offs · LoRA vs full fine-tuning · distributed inference vs distributed training

Operations

How do I monitor GPUs and LLM quality/cost? How do I upgrade GPU drivers safely? How do I troubleshoot failed GPU pods? How do I roll back a model deployment? How do I scale inference (and scale it to zero)? How do I isolate workloads (namespace, MIG, network)?

Architecture

Kubernetes vs Slurm · vLLM vs Triton vs KServe · Ray vs Kubernetes Jobs · object storage vs parallel filesystem · Ethernet vs RDMA/InfiniBand · self-managed vs managed cloud AI platforms

🚫 What NOT to Do

❌ Don’t learn every MLOps tool

You don’t need 20 frameworks, 30 platforms, 50 certifications. You need one coherent stack you can defend end-to-end in an interview or an incident.

❌ Don’t abandon DevOps

AI does not replace Linux, networking, containers, Kubernetes, Terraform, observability, security, or CI/CD. AI makes those skills more valuable in a different environment.

❌ Don’t start with advanced ML mathematics

Unless your career goal is ML research, you don’t need to begin with advanced calculus, probability, and neural-network theory. Understand the workload first.

❌ Don’t build another basic chatbot

A chatbot proves you can call an API. A platform that provisions GPUs, fine-tunes a model, deploys it, retrieves context via RAG, monitors it, scales it, tracks its cost, and recovers from failure demonstrates infrastructure engineering.

❌ Don’t ignore cost and security until “later”

On GPU infrastructure, cost mistakes are expensive fast, and inference endpoints are internet-facing attack surface from day one. Bake both in from Month 1, not as an afterthought.

💼 Career Positioning

A strong profile can be described as:

DevOps / Platform Engineer with expertise in Kubernetes, cloud infrastructure, GPU platforms and production AI workloads.

Or, more specialized:

AI Infrastructure Engineer focused on Kubernetes, GPU orchestration, AI model serving, observability, cost, and cloud automation.

Or:

MLOps / LLMOps Platform Engineer specializing in production AI infrastructure, fine-tuning pipelines, and inference cost optimization.

The title matters less than the capability underneath it:

Linux + Cloud + Containers + Kubernetes + Infrastructure as Code
  + Observability + GPU Infrastructure + AI Workloads
  + Fine-Tuning/Optimization + RAG/Vector Infra
  + MLOps/LLMOps + Security + FinOps

🎓 Certifications worth considering

Certification	Body	Relevance
CKA — Certified Kubernetes Administrator	CNCF	Core platform credibility
CKS — Certified Kubernetes Security Specialist	CNCF	Security depth
HashiCorp Certified: Terraform Associate	HashiCorp	IaC credibility
NVIDIA-accredited training/certifications	NVIDIA DLI	GPU/AI-specific credibility
AWS/GCP/Azure ML or Solutions Architect certs	Cloud vendor	If targeting managed-cloud-heavy roles

🧭 The 80/20 Version

If you have limited time, focus here first:

                    ★ AI INFRASTRUCTURE ★

                         Kubernetes
                              │
                         GPU Operator
                              │
                    NVIDIA Container Toolkit
                              │
                         CUDA / GPU
                              │
                    ┌─────────┴─────────┐
                    │                   │
                  vLLM               PyTorch
                    │                   │
                    └─────────┬─────────┘
                              │
                          AI Workload
                              │
                    Prometheus / Grafana
                              │
                    Terraform / Ansible
                              │
                       GitHub / GitOps

Then add: KServe · Ray · MLflow · PEFT/LoRA · a vector DB · NCCL · RDMA · Slurm · Ceph/Lustre · Kubecost

🧭 Recommended order if you’re starting today

Linux
  ↓
Docker
  ↓
Kubernetes
  ↓
Terraform + Ansible
  ↓
Prometheus + Grafana
  ↓
NVIDIA GPU + CUDA
  ↓
NVIDIA Container Toolkit
  ↓
GPU Operator
  ↓
DCGM
  ↓
PyTorch + Hugging Face
  ↓
vLLM
  ↓
KServe / Ray / MLflow
  ↓
GitOps + Security + FinOps
  ↓
RAG / Fine-Tuning
  ↓
NCCL / RDMA / Slurm / AI Storage

⭐ This is the shortest path from your existing DevOps skill set to a credible AI Infrastructure portfolio.

📚 Master Resource Index

Linux / Systems

Linux Kernel Docs · Red Hat Docs · Linux Foundation Training

Containers

Docker Docs · containerd Docs · NVIDIA Container Toolkit

Kubernetes

Kubernetes Docs · Scheduling & Eviction · Helm · Kustomize

NVIDIA / GPU

NVIDIA Developer · NVIDIA Docs · CUDA · cuDNN · GPU Operator · MIG Guide · MPS · DCGM · DCGM Exporter · NCCL · TensorRT-LLM · NIM

AI / ML Frameworks

PyTorch · PyTorch Tutorials · Hugging Face · HF LLM Course · Transformers · Accelerate · PEFT · DeepSpeed · bitsandbytes

Inference / Serving

vLLM · HF TGI · NVIDIA Triton · SGLang · Ollama · KServe · Ray · KubeRay

Vector DB / RAG

Milvus · Qdrant · Weaviate · pgvector · Pinecone · LangChain · LlamaIndex

MLOps / Pipelines / GitOps

MLflow · Kubeflow · Argo Workflows · Argo CD · Flux · DVC · GitHub Actions · Airflow · Prefect

LLM Observability

Langfuse · Weights & Biases · Arize Phoenix

Infrastructure as Code

Terraform · Ansible · Pulumi

Networking / HPC

NVIDIA Networking · NVIDIA Networking Docs · Slurm · Ceph · Lustre

Observability & Cost

Prometheus · Grafana · Kubecost · FinOps Foundation

Autoscaling

Karpenter · Cluster Autoscaler · KEDA

Registries / Supply Chain

Harbor · Sigstore/Cosign · Trivy

Security

Kubernetes Security · OPA Gatekeeper · Falco · HashiCorp Vault

Managed Cloud AI

AWS SageMaker · Google Vertex AI · Azure ML

Certifications

CKA · CKS · Terraform Associate · NVIDIA DLI Training

Communities & Ongoing Learning

CNCF · KubeCon + CloudNativeCon · NVIDIA GTC · r/MachineLearning · r/mlops · Hugging Face Forums

✅ Definition of Done

You do not need to finish every line in this document before applying for AI infrastructure roles.

A strong baseline is reached when you can independently:

🐧 Operate Linux hosts and diagnose failures.
📦 Build and troubleshoot containers.
☸️ Run production-style Kubernetes workloads.
🎮 Provision and expose NVIDIA GPUs to workloads.
🛠️ Operate GPU Operator and device plugins.
📊 Monitor GPU health, utilization, and workload behavior.
🚄 Deploy and scale an LLM with vLLM.
🔄 Automate infrastructure and deployments with IaC + GitOps.
🔐 Apply practical security controls.
💰 Explain and measure GPU cost.
🧠 Understand enough AI/ML to operate the workloads intelligently.
🧪 Demonstrate all of the above through a documented project.

🚀 If you can do those twelve things, you are no longer merely “learning MLOps.” You are demonstrating AI infrastructure engineering capability.

🏁 Final Objective

🚀 The finish line is not knowing AI. It is being able to operate AI infrastructure.

The end goal is not: “I know AI.”

It is: “I can build, automate, deploy, secure, observe, cost-manage, troubleshoot, and scale infrastructure that runs AI workloads.”

A mature AI infrastructure engineer should be able to take:

Bare Infrastructure
       ↓
GPU Nodes
       ↓
Linux + NVIDIA Stack
       ↓
Kubernetes
       ↓
GPU Operator
       ↓
GPU Scheduling + Autoscaling
       ↓
Observability + Cost Visibility
       ↓
Fine-Tuning / Optimization
       ↓
Model Serving + RAG
       ↓
CI/CD + GitOps + Security
       ↓
Scaling
       ↓
Distributed AI
       ↓
Production Operations

…and understand the major failure modes at every layer. That is the practical intersection of DevOps, Platform Engineering, MLOps, LLMOps, and AI Infrastructure.

Recommended Page Metadata

Title

DevOps to AI Infrastructure: A Complete, Practical Roadmap

Slug

devops-to-ai-infrastructure-roadmap

Description

A practical, infrastructure-first roadmap for DevOps engineers moving into GPU platforms, Kubernetes AI workloads, fine-tuning, RAG, MLOps, LLMOps, security, cost management, and AI infrastructure engineering.

Category

DevOps / AI Infrastructure

Tags

DevOps
AI Infrastructure
MLOps
LLMOps
Kubernetes
GPU
NVIDIA
CUDA
vLLM
RAG
Fine-Tuning
Platform Engineering
FinOps
Cloud

DevOps to AI Infrastructure Engineering — The Complete Roadmap

🚀 DevOps to AI Infrastructure Engineering

🧠 From Linux → Kubernetes → GPUs → AI Platforms

⚡ Quick Navigation

🗺️ Roadmap by Phase

🎯 The Destination

🧭 How to Use This Roadmap

🏗️ The AI Infrastructure Stack

🥇 Priority Map

🎯 How to use the priorities

🐧 01 — Linux: The Foundation

📌 Learn

🧰 Tools

Why it matters

📚 Resources

🏁 Job-Ready Checkpoint

📦 02 — Containers

📌 Learn

🧰 Tools

📚 Resources

🏁 Job-Ready Checkpoint

☸️ 03 — Kubernetes: Your Main Platform

📌 Learn deeply

🧰 Tools

📚 Resources

🏁 Job-Ready Checkpoint

🎮 04 — GPU Fundamentals

📌 Learn

📚 Resources

🏁 Job-Ready Checkpoint

⚡ 05 — CUDA

📌 Learn

📚 Resources

🧩 06 — NVIDIA Container Toolkit

📌 Learn

📚 Resources

🛠️ 07 — NVIDIA GPU Operator ⭐

📌 Learn

📚 Resources

🏁 Job-Ready Checkpoint

📅 08 — GPU Scheduling & Autoscaling

📌 Learn — Scheduling

📌 Learn — Autoscaling (often missing from GPU roadmaps)

📚 Resources

📊 09 — GPU Observability ⭐

📈 Monitor

🛠️ Build

📚 Resources

🏁 Job-Ready Checkpoint

🧠 10 — AI Fundamentals for Infrastructure Engineers

Understand

📚 Resources

🔥 11 — PyTorch

📌 Learn

📚 Resources

🤗 12 — Hugging Face

📌 Learn

📚 Resources

🎛️ 13 — Fine-Tuning & Model Optimization (NEW)

📌 Learn

📚 Resources

🚄 14 — Inference Serving Engines

vLLM ⭐ (🟢 Core)

Other engines worth knowing (🟡 working knowledge)

🏁 Job-Ready Checkpoint

🎯 15 — KServe

📌 Learn

📚 Resources

⚙️ 16 — Ray

📌 Learn

📚 Resources

🔎 17 — Vector Databases & RAG (NEW)

📌 Learn

🧰 Tools

📚 Resources

🔬 18 — MLOps & Pipeline Orchestration

🧰 Tools

MLflow (🟢 Core)

Argo Workflows (🟢 Core)

Kubeflow Pipelines (🟡 Working knowledge)