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Kubernetes Foundations for ML

Kubernetes for AI/ML: The 2026 Landscape

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Kubernetes has become the de facto operating layer for AI-driven services. With 54% adoption for AI/ML workloads and 70%+ of enterprises running large AI systems on Kubernetes, understanding this platform is essential for any ML engineer.

Market Reality

Kubernetes Market Growth

Metric 2025 2030 CAGR
Market Size $2.57B $7.07B 22.4%
Container Orchestration Share 92% 95%+ -
Production Deployment 80%+ 90%+ -

AI/ML Workload Trends:

  • 54% of organizations run AI/ML on Kubernetes (Spectro Cloud 2025)
  • 90%+ teams expect ML workload growth in next 12 months
  • 45% embedding AI-driven workload balancing
  • "Kubernetes AI" search volume increased 300% in 2025

Why Kubernetes Dominates ML

┌────────────────────────────────────────────────────────────────┐
│                    ML Platform Requirements                     │
├────────────────────────────────────────────────────────────────┤
│  Scalability     │  Training jobs: 1 → 1000 GPUs               │
│  Resource Mgmt   │  GPUs, TPUs, high-memory nodes              │
│  Reproducibility │  Containerized environments                  │
│  Multi-tenancy   │  Teams share cluster resources              │
│  Portability     │  On-prem ↔ Cloud ↔ Edge                     │
│  Ecosystem       │  Kubeflow, KServe, MLflow, Airflow          │
└────────────────────────────────────────────────────────────────┘
                              ↓
                    Kubernetes Provides All

Kubernetes Evolution for AI/ML

Key Milestones (2024-2026)

Version Release AI/ML Features
1.32 Dec 2024 Memory Manager GA
1.33 Apr 2025 DRA Beta, In-Place Pod Resize Beta
1.34 Aug 2025 DRA GA, OCI Images as Volumes
1.35 Dec 2025 KYAML Beta, Enhanced DRA

Kubernetes 1.34: The AI/ML Milestone

Dynamic Resource Allocation (DRA) GA:

# ResourceClaim for GPU allocation
apiVersion: resource.k8s.io/v1
kind: ResourceClaim
metadata:
  name: gpu-claim
spec:
  devices:
    requests:
    - name: gpu
      deviceClassName: nvidia-gpu
      count: 2
---
# Pod using ResourceClaim
apiVersion: v1
kind: Pod
metadata:
  name: training-job
spec:
  resourceClaims:
  - name: gpu
    resourceClaimName: gpu-claim
  containers:
  - name: trainer
    image: my-training-image:latest
    resources:
      claims:
      - name: gpu

Key DRA Benefits:

  • Just-in-time GPU/TPU selection and allocation
  • Multi-pod device sharing
  • Consumable device capacity tracking
  • Reduced hardware costs for AI/ML workloads

OCI Images as Volumes:

# Load ML model weights without custom base images
apiVersion: v1
kind: Pod
metadata:
  name: inference-server
spec:
  containers:
  - name: model-server
    image: kserve/serving:latest
    volumeMounts:
    - name: model-weights
      mountPath: /models
  volumes:
  - name: model-weights
    image:
      reference: myregistry/llama-7b-weights:v1
      pullPolicy: IfNotPresent

ML Workload Categories

Training vs Inference

Aspect Training Inference
Duration Hours to days Milliseconds
Resources High GPU, bursty Consistent, lower
Scaling Job-based Autoscaling
Pattern Batch Request-response
K8s Resource Job/CronJob Deployment/Service

Kubernetes Resources for ML

Training Pipeline:
┌─────────────┐    ┌─────────────┐    ┌─────────────┐
│    Job      │ → │ PersistentVC │ → │   Secret    │
│ (Training)  │    │ (Data/Model)│    │ (Registry)  │
└─────────────┘    └─────────────┘    └─────────────┘

Inference Stack:
┌─────────────┐    ┌─────────────┐    ┌─────────────┐
│ Deployment  │ → │   Service   │ → │   Ingress   │
│ (Model)     │    │ (Internal)  │    │ (External)  │
└─────────────┘    └─────────────┘    └─────────────┘

ML Platform Architecture on Kubernetes

Reference Architecture

┌─────────────────────────────────────────────────────────────────┐
│                     ML Platform on Kubernetes                    │
├─────────────────────────────────────────────────────────────────┤
│  User Layer        │  Notebooks │ Pipelines │ Model Registry    │
├─────────────────────────────────────────────────────────────────┤
│  ML Layer          │  Kubeflow  │  MLflow   │  KServe │ Feast   │
├─────────────────────────────────────────────────────────────────┤
│  Platform Layer    │  Istio     │  ArgoCD   │  Prometheus       │
├─────────────────────────────────────────────────────────────────┤
│  Kubernetes Layer  │  Scheduler │  DRA      │  CNI    │ CSI     │
├─────────────────────────────────────────────────────────────────┤
│  Infrastructure    │  GPU Nodes │  Storage  │  Network          │
└─────────────────────────────────────────────────────────────────┘

Cloud Provider ML Kubernetes Services

Feature EKS GKE AKS
GPU Nodes P4d, P5, G5 A100, H100, TPU NC, ND series
ML Addon SageMaker Operators Vertex AI Azure ML Extension
Autopilot Karpenter GKE Autopilot KEDA
AI Conformance Certified Certified Certified

Next, we'll explore Kubernetes architecture and core concepts essential for ML workloads. :::

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