Kubernetes for AI Workloads: A Practical Guide

Kubernetes has become the de facto platform for deploying AI and machine learning workloads. But running ML on Kubernetes requires understanding its unique requirements.

Why Kubernetes for AI?

1. Scalability

AI workloads have variable resource demands:

• Training: Burst of GPU compute

• Inference: Scales with user traffic

• Batch processing: Scheduled heavy workloads

Kubernetes handles all of these patterns.

2. Resource Management

ML workloads have specific hardware requirements:

• GPU access for training and inference

• High memory for large models

• Fast storage for datasets

Kubernetes provides fine-grained resource allocation.

3. Reproducibility

Same container, same results. Kubernetes ensures your model runs identically across development, staging, and production.

4. Integration

Kubernetes integrates with the entire ML ecosystem:

• Model serving frameworks

• Experiment tracking

• Data pipelines

• Monitoring tools

Key Concepts for ML on Kubernetes

GPU Scheduling

Kubernetes can schedule pods to nodes with GPUs:

resources:
  limits:
    nvidia.com/gpu: 1

Requirements:

• GPU nodes in your cluster

• NVIDIA device plugin installed

• Container runtime configured for GPU access

Persistent Volumes

ML workloads need persistent storage:

volumeMounts:
  - name: model-storage
    mountPath: /models
  - name: data-cache
    mountPath: /data

Use appropriate storage classes:

• SSD for fast model loading

• Cheaper storage for datasets

• ReadWriteMany for shared data

Resource Requests and Limits

Right-size your workloads:

resources:
  requests:
    memory: "4Gi"
    cpu: "2"
  limits:
    memory: "8Gi"
    cpu: "4"

Horizontal Pod Autoscaling

Scale inference based on demand:

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: model-inference
  minReplicas: 2
  maxReplicas: 20
  metrics:
    - type: Resource
      resource:
        name: cpu
        target:
          type: Utilization
          averageUtilization: 70

Model Serving Options

KServe (formerly KFServing)

The Kubernetes-native model serving platform:

apiVersion: serving.kserve.io/v1beta1
kind: InferenceService
metadata:
  name: sklearn-model
spec:
  predictor:
    sklearn:
      storageUri: "s3://bucket/model"

Features:

• Autoscaling (including scale to zero)

• Canary deployments

• Multi-model serving

• Request batching

Seldon Core

Enterprise-grade ML deployment:

apiVersion: machinelearning.seldon.io/v1
kind: SeldonDeployment
metadata:
  name: model-deployment
spec:
  predictors:
    - graph:
        implementation: SKLEARN_SERVER
        modelUri: s3://bucket/model

Features:

• A/B testing

• Explainability

• Outlier detection

• Rich metrics

Custom Deployments

Sometimes simpler is better:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: custom-model
spec:
  replicas: 3
  template:
    spec:
      containers:
        - name: model
          image: my-registry/my-model:v1.2.3
          ports:
            - containerPort: 8080

When to use:

• Simple requirements

• Custom preprocessing

• Non-standard serving patterns

Architecture Patterns

Pattern 1: Inference Service

[Load Balancer] → [Ingress] → [Model Pods] → [Model Storage]

Standard pattern for real-time inference. Add HPA for auto-scaling.

Pattern 2: Batch Processing

[CronJob/Queue] → [Training Pods] → [Model Registry]
                                   ↓
                              [Inference Pods]

Scheduled or event-triggered model training, separate from serving.

Pattern 3: Multi-Model

[Router] → [Model A Pods]
        → [Model B Pods]
        → [Model C Pods]

Route requests to different models based on criteria (A/B testing, use case, etc.).

Practical Considerations

Cold Start Times

Large models take time to load. Mitigate with:

• Minimum replica counts

• Model caching

• Pre-warming strategies

• Smaller, distilled models for low-latency cases

Cost Optimisation

GPU nodes are expensive:

• Use spot/preemptible nodes for training

• Right-size GPU instances

• Consider CPU inference for some models

• Scale to zero when possible

Security

ML workloads process sensitive data:

• Network policies between namespaces

• Pod security standards

• Secrets management for credentials

• Image scanning and signing

Monitoring

Essential metrics:

• Inference latency (p50, p95, p99)

• Prediction throughput

• Model accuracy (requires custom metrics)

• Resource utilisation

• Error rates

Our Kubernetes ML Stack

We typically implement:

Getting Started

If you're new to Kubernetes ML:

1. Start simple: Deploy a basic model without complex frameworks

2. Add observability: Understand what's happening before optimising

3. Implement CI/CD: Automate deployments early

4. Scale gradually: Add complexity as requirements demand

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Need help deploying AI on Kubernetes? We can help.