MLOps
Multi-Cloud ML Architecture Patterns

Multi-Cloud ML Architecture Patterns

Bharat Aameriya's photo
Bharat Aameriya
·

2 min read

As machine learning (ML) systems grow in complexity, organizations are increasingly adopting multi-cloud strategies to ensure flexibility, cost efficiency, and high availability. Multi-cloud ML architectures allow leveraging different cloud providers for various components of the ML pipeline, enhancing performance and reducing vendor lock-in.

Why Use Multi-Cloud for ML?

  • Avoid Vendor Lock-in: Use services from different cloud providers without dependency on one.

  • Cost Optimization: Select cost-effective compute/storage solutions across providers.

  • High Availability: Deploy ML models across multiple clouds for fault tolerance.

  • Regulatory Compliance: Meet data residency and governance requirements.

Common Multi-Cloud ML Architecture Patterns

1. Hybrid Cloud ML Deployment

  • Train ML models on-premises using GPUs for cost efficiency.

  • Deploy inference endpoints on public cloud providers (AWS, GCP, Azure) for scalability.

2. Cross-Cloud Data Pipelines

  • Store data on Google Cloud Storage (GCS) for cost efficiency.

  • Use AWS Lambda for pre-processing.

  • Train models on Azure ML using data streamed from GCS.

3. Distributed Model Training Across Clouds

  • Train a model across multiple clouds using federated learning.

  • Use PyTorch Distributed Data Parallel (DDP) for cross-cloud GPU training.

Example: Cross-Cloud Model Training with Kubernetes

Step 1: Define Kubernetes Clusters on AWS and GCP

apiVersion: v1
kind: Namespace
metadata:
  name: ml-pipeline
---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: ml-training
  namespace: ml-pipeline
spec:
  replicas: 2
  selector:
    matchLabels:
      app: ml-model
  template:
    metadata:
      labels:
        app: ml-model
    spec:
      containers:
      - name: ml-training
        image: gcr.io/my-project/ml-training:latest
        resources:
          limits:
            nvidia.com/gpu: 1

Step 2: Deploy and Manage Across Clouds

kubectl --context aws apply -f ml-training.yaml
kubectl --context gcp apply -f ml-training.yaml

Step 3: Monitor and Scale Resources

kubectl get pods --all-namespaces
kubectl scale deployment ml-training --replicas=5

Conclusion

Multi-cloud ML architectures provide flexibility, cost savings, and reliability. By leveraging Kubernetes, federated learning, and cross-cloud pipelines, teams can optimize their ML workflows and ensure seamless scalability across cloud providers.

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