Project 1: The Production AI Inference Pipeline

You have spent the last 13 modules learning the individual components of Kubernetes. You know how to pods work (Module 3), how they talk to each other (Module 5), how they scale (Module 8), and how they are secured (Module 10).

But in the professional world, these pieces don't exist in isolation. They are part of a larger System.

In this first real-world project, we are going to build a Production AI Inference Pipeline. This is the exact architecture used by companies like OpenAI, Anthropic, and Midjourney to serve millions of AI requests per second.

Our pipeline will feature:

A Frontend: A Next.js UI for user interaction.
A Gateway: A FastAPI backend that handles authentication and rate limiting.
A Message Queue: Redis used to manage the "Work Queue" of AI requests.
The Workers: GPU-accelerated Python pods that pull work from Redis and run the Llama 3 model.
The Brain: Automated scaling that spins up more GPU workers when the Redis queue gets too long.

1. The Architecture Blueprint

Before we touch a single line of YAML, we must visualize the flow of data.

graph TD
    User["User (Browser)"] -- "HTTPS" --> Ing["Nginx Ingress"]
    Ing -- "Route /api" --> API["FastAPI Gateway"]
    
    subgraph "The Control Layer"
        API -- "Enforce Policy" --> RBAC["K8s RBAC"]
        API -- "Cache Results" --> Redis["Redis Cluster"]
    end
    
    subgraph "The Inference Layer (GPU)"
        Worker1["GPU Worker 1 (Llama-3)"] -- "Pop Task" --> Redis
        Worker2["GPU Worker 2 (Llama-3)"] -- "Pop Task" --> Redis
    end
    
    HPA["Custom Metric HPA"] -- "Monitor Queue Length" --> Redis
    HPA -- "Scale Up/Down" --> Worker1
    
    style Worker1 fill:#f96,stroke:#333
    style Worker2 fill:#f96,stroke:#333
    style Redis fill:#9cf,stroke:#333

2. Step 1: Deploying the Redis "Brain"

We will use a StatefulSet (Module 6.2) for Redis to ensure data persistence even if the pods restart.

apiVersion: apps/v1
kind: StatefulSet
metadata:
  name: redis
spec:
  serviceName: redis
  replicas: 1
  template:
    spec:
      containers:
      - name: redis
        image: redis:7-alpine
        ports:
        - containerPort: 6379

3. Step 2: The FastAPI Gateway (The Bouncer)

Our gateway will handle the "Ingress" and security. It will be a standard Deployment (Module 4).

Key Features:

SecurityContext: (Module 10.3) Runs as a non-root user.
ServiceAccount: (Module 10.2) Has a dedicated account with permissions only to read ConfigMaps.
Probes: (Module 4.2) Readiness and Liveness probes to ensure the gateway is healthy.

4. Step 3: The GPU Workers (The Muscle)

This is the most complex part. These pods need specialized hardware and large model files.

Configuration:

Resource Requests: (Module 4.5) nvidia.com/gpu: 1.
Init Container: (Module 3.2) Used to download the 5GB Llama-3 weights from S3 before the main app starts.
Affinity: (Module 8) We use nodeAffinity to ensure these pods ONLY land on nodes that actually have NVIDIA hardware.

5. Step 4: Zero-Trust Networking

We don't want the "Frontend" to be able to talk directly to the "Redis" database. Only the "Gateway" and "Workers" should have access.

apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
  name: redis-isolation
spec:
  podSelector:
    matchLabels:
      app: redis
  ingress:
  - from:
    - podSelector:
        matchLabels:
          app: ai-gateway
    - podSelector:
        matchLabels:
          role: ai-worker

6. Step 5: The "Pro" Move - Custom Metric Scaling

Standard CPU-based scaling doesn't work well for AI. An AI model might use 100% of the GPU even if there is only 1 request in the queue. We want to scale based on Queue Depth.

The Workflow:

Prometheus Adapter: (Module 9.2) Scrapes Redis for the llen (list length) of the queue.
Custom Metric: We expose redis_queue_length to Kubernetes.
HPA: We tell the HPA: "If queue length > 10 per worker, spin up another worker."

7. Operational Checklist: Moving to Production

Before you hand the keys to your users, you must verify:

Logs: Are you exporting logs to Loki (Module 9.3)?
Tracing: Can you see the "Latency" of a request from the UI to the GPU using Jaeger (Module 9.4)?
Backups: Are you running Velero (Module 13.5) on your Redis volume?
Costs: Have you set Resource Quotas (Module 8.1) so a bug in your code doesn't spend $10,000 on GPUs in one night?

8. Project Summary and Key Takeaways

System Design: A great K8s architect thinks about the flow between services, not just individual pods.
Isolation: Use NetworkPolicies and RBAC to create a secure, multi-layered environment.
Resource Management: GPU nodes are expensive; use affinity and custom scaling to optimize their usage.
Observability: You cannot manage what you cannot measure. Dashboarding is mandatory.

In the next project, we will look at the business side of Kubernetes: Building a Multi-tenant SaaS Platform.

9. SEO Metadata & Keywords

Focus Keywords: building AI inference pipeline Kubernetes, FastAPI K8s project, K8s GPU worker configuration, scaling Kubernetes with Redis queue length, production-ready AI architecture K8s, Kubernetes capstone project.

Meta Description: Build a professional, production-grade AI inference pipeline on Kubernetes. Learn to integrate FastAPI, Redis, and GPU-accelerated workers into a secure, scalable, and observable system using industry best practices.

Project 1: Building a Production AI Inference Pipeline