Building AI Infrastructure, Part 1: The 8 Building Blocks Explained Simply

Watch the video: Building AI Infrastructure, Part 1 on YouTube

Welcome to Part 1 of a series where we build real AI infrastructure, step by step, on AWS, Azure, Google Cloud, and on-premises. Simple enough for a beginner. Solid enough for production.

Today there is no code. Today we learn the map. By the end of this post, you'll be able to look at almost any AI platform and name every major part of it.

Why this matters

Every AI product does the same five things:

It takes in data.
It trains a model.
It saves that model.
It serves the model to answer requests.
People (or other systems) use the answers.

AI infrastructure is everything underneath those five steps that makes them run reliably, safely, and at a fair cost.

Here's the big idea for the whole series: the model is maybe 10%. The other 90% is everything that runs it.

The mental model: a restaurant kitchen

To keep this simple, picture a restaurant kitchen:

Kitchen	AI platform
The stove	Compute
The pantry	Data & storage
Hallways and doors	Networking
The head chef	Orchestration
The recipe book	MLOps
The serving window	Serving
The inspector	Observability
Locks, keys, and the budget	Guardrails

Eight blocks. Let's walk through them one at a time.

Block 1: Compute (the stove)

This is where the cooking happens.

CPUs run the everyday work: APIs, data jobs, and small models.
GPUs do the heavy math. Training and large models need thousands of small calculations at once, and that is exactly what GPUs are built for.
GPUs are expensive. Good AI infrastructure doesn't just buy them. It shares them and turns them off when they're idle.

Where it lives: AWS GPU instances (G5, P5) · Azure NC and ND GPU VMs · Google Cloud A2 and G2 machines (A100, L4 GPUs) · On-prem: your own NVIDIA GPU servers.

Block 2: Data & storage (the pantry)

Ingredients, labeled and in the right place.

Object storage holds the raw data. Files, images, logs, and training sets go here because it's cheap and nearly unlimited.
Models are saved here too. Every trained model is just a file with a version number.
Faster disks feed the GPUs. A GPU waiting for data is money burning.

Where it lives: AWS S3 · Azure Blob Storage / Data Lake · Google Cloud Storage · On-prem: MinIO and Ceph.

Block 3: Networking (hallways and doors)

Who can get where?

The platform gets its own private network: your own space in the cloud, cut into smaller pieces called subnets.
Users come in through one door: a load balancer that sends each request to the right place.
Everything else stays private. Your data and your GPUs are never directly on the internet.

Where it lives: AWS VPC + load balancer · Azure VNet + load balancer · Google Cloud VPC + load balancer · On-prem: VLANs, a firewall, and MetalLB.

Block 4: Orchestration (the head chef)

Who cooks what, on which stove?

Apps run in containers. A container packs an app with everything it needs, so it behaves the same everywhere.
Kubernetes runs those containers. It places them, restarts them when they crash, and adds more when things get busy.
The special move for AI: GPU node pools. A separate group of GPU machines that grows from zero when work arrives, then shrinks back when it's done. This is the heart of an efficient platform.

Where it lives: AWS EKS · Azure AKS · Google GKE · On-prem: OpenShift or plain Kubernetes.

Block 5: MLOps (the recipe book)

Test it, write it down, improve it. Training a model once by hand is a project. Training it the same way every time is a platform.

Pipelines train the model with the same steps every run: prepare the data, train, test.
A registry keeps every version: which data, which settings, which score.
Gates protect production, so a worse model never replaces a better one.

Where it lives: AWS SageMaker · Azure Machine Learning · Google Vertex AI · Anywhere (including on-prem): the open-source pair MLflow and Kubeflow.

Block 6: Serving (the serving window)

Where plates go out fast and hot.

The model becomes an API. An app sends a question; the model sends back an answer.
It scales with demand. More copies when it's busy, fewer when it's quiet.
New versions go out slowly. Send 10% of traffic to the new version first. If it's healthy, give it the rest. That careful first step is called a canary.

Where it lives: The answer is the same everywhere: KServe or vLLM running on EKS, AKS, GKE, or OpenShift.

Block 7: Observability (the inspector)

The inspector and the dashboard on the wall.

Collect metrics, logs, and traces. Metrics are numbers over time. Logs are records of events. Traces follow the path of each request so you can see where time was spent.
An SLO defines "good enough." For example: 99.9% of requests succeed.
Alerts wake the right person, but only when users are really affected. That's how teams stay informed without burning out.

Where it lives: AWS CloudWatch + Prometheus · Azure Monitor + Prometheus · Google Cloud Monitoring + Prometheus · On-prem: Prometheus, Grafana, and OpenTelemetry.

Block 8: Guardrails (locks, keys, and budget)

Identity with least privilege. Every person and every app gets only the access it needs.
Secrets never live in code. Passwords and API keys go into a vault.
Cost is watched every day. Budgets, alerts, and idle GPUs turned off.

These are not optional extras. They wrap around the whole platform from day one.

Where it lives: AWS IAM, Secrets Manager, Budgets · Azure Entra ID, Key Vault, Cost Management · Google Cloud IAM, Secret Manager, Budgets · On-prem: RBAC, Vault, and OpenCost.

Putting it all together

Think of it as a stack:

At the bottom: compute, data, and network.
On top of that: orchestration (Kubernetes + GPU node pools).
On top of Kubernetes: the two AI layers. MLOps to train, track, and gate models, and Serving for APIs, scaling, and canaries.
Observability watches every layer.
Guardrails wrap around everything: identity, secrets, and cost.

And here's the key simplifier for the whole series: as we move from cloud to cloud, only the bottom layer really changes. Learn the pattern once, and you can run it anywhere.

The series: one platform, four homes

Part 1: The building blocks (this post)
Part 2: The foundation, on your laptop, so you understand every layer before you pay for cloud
Part 3: AWS (EKS + GPUs)
Part 4: Azure (AKS + GPUs)
Part 5: Google Cloud (GKE + GPUs)
Part 6: On-premises, in a data center
Part 7: One platform across all four, managed from Git

In every part, the code is on GitHub. You build along, then tear it down, so your cloud bill stays near zero.

Get ready for Part 2

Before the next video, do these four things:

Install the tools: Docker, kubectl, Terraform, and Git.
Create three cloud accounts: AWS, Azure, and Google Cloud, using their free tiers.
Set a budget alert on each one first, before anything else. Even $10 is fine.
Request GPU quota early. New accounts start at zero GPUs.

Final recap

The eight blocks are: compute, data, network, orchestration, MLOps, serving, observability, and guardrails.

If you can name those eight, you can walk into almost any AI platform conversation and understand what's really there. Next, in Part 2, we build all eight on your laptop.

Labs and build-along support: transformed2succeed.com
Code: github.com/Here2ServeU
Watch Part 1: YouTube