Understanding Internal Working for Real-World MLOps

Because:

The better you understand the architecture, the easier it becomes to debug, optimize, and scale real-world ML systems.

---

Why Understanding KServe Architecture Matters

Most engineers can:

• deploy a model

• run inference

• expose endpoints

But when something breaks:

• model not loading

• scaling not happening

• endpoint not reachable

• latency issues

👉 That’s when architecture knowledge becomes critical.

---

Two Deployment Modes in KServe

KServe supports two main networking approaches:

1. Ingress-based deployment (traditional)

2. Gateway API-based deployment (recommended)

Let’s understand both.

---

1 — Standard Deployment Using Kubernetes Ingress

Image

---

How It Works

When you create an InferenceService, KServe controller takes over.

Step-by-step flow:

1. You apply:

kind: InferenceService

1. KServe Controller detects it

2. It automatically creates:

• Kubernetes Deployment (model server)

• Service

• Ingress resource

• Autoscaling components (HPA/KEDA)

1. Traffic flows like this:

Client → Ingress → Service → Pod → Model Server → Prediction

---

Key Components Explained

KServe Controller

• Watches InferenceService

• Creates all required resources

• Acts like an operator

---

Model Server Pod

• Runs your ML model

• Handles prediction requests

• Loads model from storage (S3/GCS)

---

Autoscaling Layer

• HPA → CPU/Memory-based scaling

• KEDA → Event-based scaling

---

Storage Layer

• Persistent Volume (model cache)

• Avoids re-downloading model every time

---

Limitations of Ingress-Based Mode

This is why it is not recommended anymore:

• No advanced traffic splitting

• Limited protocol support

• No advanced routing

• Less flexibility

---

2 — Standard Deployment Using Gateway API (Recommended)

---

What Changes Here?

Instead of using Ingress, KServe uses:

👉 Gateway API + HTTPRoute

This is the modern Kubernetes networking standard.

---

Flow Explanation

Client → Gateway → HTTPRoute → Service → Pod → Model Server

---

Key Differences

HTTPRoute (Instead of Ingress)

• Advanced routing

• Better traffic control

• Supports modern networking features

---

Gateway

• Central traffic entry point

• More flexible than Ingress

• Supports multi-tenant setups

---

Why This Is Better

• Supports traffic splitting (canary deployments)

• Better observability

• Future-proof Kubernetes standard

• More control over routing

---

What Happens Internally When You Deploy a Model

Let’s simplify everything into a real flow.

---

Step 1 — You Create InferenceService

kind: InferenceService

---

Step 2 — KServe Controller Takes Action

It creates:

• Deployment

• Service

• Autoscaler

• Networking layer

---

Step 3 — Model Server Starts

• Pulls model from storage

• Loads into memory

• Starts inference endpoint

---

Step 4 — Traffic Routing

Depending on mode:

• Ingress → Service

  OR

• Gateway → HTTPRoute → Service

---

Step 5 — Scaling Happens Automatically

• Low traffic → scale down

• No traffic → scale to zero

• High traffic → scale up

---

Real-World Example

Imagine:

You deploy a fraud detection model.

During normal hours:

• 10 requests/sec

• 2 pods running

During peak hours:

• 1000 requests/sec

• KServe scales to 20 pods

During night:

• No traffic

• KServe scales to zero

👉 This is automatic. No manual scaling needed.

---

Key Components Summary

Component	Responsibility
InferenceService	User-defined model deployment
KServe Controller	Creates and manages resources
Model Server Pod	Runs model and handles inference
Service	Internal routing
Ingress / Gateway	External access
HPA / KEDA	Autoscaling
Storage (PVC)	Model caching

---

Ingress vs Gateway — Final Comparison

Feature	Ingress	Gateway API
Routing flexibility	Limited	Advanced
Traffic splitting	No	Yes
Protocol support	Limited	Better
Future support	Legacy	Recommended
Observability	Basic	Advanced

---

Key Takeaways

• KServe simplifies ML deployment, but under the hood it uses Kubernetes primitives

• Understanding controller behavior is critical for debugging

• Gateway API is the future of KServe networking

• Autoscaling is deeply integrated into KServe

• Model deployment becomes declarative and standardized

---

Why This Matters for You

If you are:

• DevOps Engineer → You understand infra → now add ML layer

• MLOps Engineer → You need to debug pipelines

• Platform Engineer → You design ML platforms

👉 This knowledge helps you:

• troubleshoot issues faster

• design better architectures

• optimize performance

• reduce production risks

---