24 Multicloud Mlops

Chapter 24: Multi-Cloud MLOps🔗

Why Multi-Cloud?🔗

  • Avoid vendor lock-in
  • Use best service from each provider
  • Compliance (data residency requirements)
  • Cost optimization (use cheapest GPU per region)

Portability Strategies🔗

USE OPEN STANDARDS:
  KFP SDK → runs on Kubeflow, Vertex AI, or locally
  MLflow  → runs anywhere
  Docker  → runs on any cloud container platform
  DVC     → works with GCS, S3, Azure Blob equally

ABSTRACT INFRASTRUCTURE:
  Terraform → provision GCP, AWS, Azure with same code
  Kubernetes → runs on GKE, EKS, AKS equally
  Helm charts → deploy same apps to any K8s

AVOID CLOUD-SPECIFIC LOCK-IN:
  ❌ Vertex AI AutoML → use MLflow + Optuna instead (portable)
  ✅ BigQuery → OK if GCP-only; use Delta Lake for multi-cloud
  ❌ SageMaker Pipelines → use Kubeflow Pipelines (portable)

Multi-Cloud Architecture🔗

Training:  AWS (cheapest A100 spot)
Data:      GCP (BigQuery for analytics)
Serving:   GCP (GKE, lower egress cost)
Monitoring: Open source (Prometheus/Grafana) on any cloud

Next → Chapter 25: Model Serving Strategies