Migrating AI workloads to the cloud is not simply lifting VMs into EC2. AI workloads have specific requirements around GPU availability, data locality, training pipeline orchestration, and model serving that make migration planning different from typical application migrations. This guide covers the practical steps for migrating AI and ML workloads to cloud platforms.

Assessment Phase

Inventory Your AI Workloads

Document every AI workload currently running on-premise:

Training workloads. What models are being trained? How often? What hardware (GPUs, CPUs, memory) do they require? What is the training data volume? Where does training data live?

Inference workloads. What models are serving predictions? What are the latency requirements? What is the throughput (requests per second)? What is the availability SLA?

Data pipelines. What data processing happens before training and inference? What tools are used (Spark, Pandas, custom scripts)? What is the data volume?

Experiment infrastructure. What tools do data scientists use (notebooks, experiment tracking, visualization)? Where do they store artifacts?

Evaluate Cloud Readiness

For each workload, assess migration complexity:

Data gravity. Where is the data? If training data is on-premise, moving it to the cloud may be the biggest migration challenge. For multi-terabyte datasets, network transfer can take days or weeks.

Latency sensitivity. If inference serves internal applications, moving to the cloud adds network latency. If downstream systems remain on-premise, this latency may be unacceptable.

Regulatory constraints. Data residency requirements, industry regulations, and security policies may restrict which cloud regions and services can be used.

GPU requirements. Map on-premise GPU hardware to cloud equivalents. On-premise NVIDIA A100 maps to AWS p4d instances. V100 maps to p3 instances. Availability varies by region.

Planning Phase

Migration Strategy per Workload

Rehost (lift and shift). Move the workload to cloud VMs with minimal changes. Fastest migration but does not leverage cloud-native services. Suitable for workloads that are already containerized or well-automated.

Replatform. Move to cloud-managed services that replace on-premise infrastructure. Replace on-premise Kubernetes with EKS, on-premise MLflow with SageMaker Experiments, on-premise GPU servers with SageMaker Training. Moderate effort, significant operational benefit.

Refactor. Redesign the workload to be cloud-native. Replace custom training pipelines with SageMaker Pipelines, replace on-premise model serving with SageMaker Endpoints or Bedrock. Highest effort, highest long-term benefit.

Migration Order

Migrate workloads in this order to minimize risk and maximize learning:

  1. Development and experimentation environments. Low risk, immediate productivity gains. Data scientists get access to on-demand GPU instances.
  2. Batch training workloads. Can tolerate downtime during migration. Validate that training produces equivalent models.
  3. Data pipelines. Move data processing to the cloud, establishing the data foundation for inference migration.
  4. Inference workloads. Highest risk because they serve production traffic. Migrate last, using blue-green or canary deployment.

Data Migration Plan

Small datasets (under 100GB). Transfer over the network using AWS CLI or SDK. Takes hours.

Medium datasets (100GB - 10TB). Use AWS DataSync for optimized network transfer. Or use S3 Transfer Acceleration.

Large datasets (10TB+). Use AWS Snowball or Snowball Edge for physical data transfer. Or set up a dedicated network connection (AWS Direct Connect) for ongoing data synchronization.

Ongoing synchronization. If training data continues to be generated on-premise during migration, set up continuous replication (AWS DMS for databases, DataSync for files) to keep cloud copies current.

Execution Phase

Environment Setup

Network architecture. Set up VPC, subnets, security groups, and connectivity to on-premise systems (VPN or Direct Connect). AI workloads often need access to on-premise data sources during the transition.

IAM and security. Create IAM roles for training jobs, inference endpoints, and data access. Follow least-privilege principles. Set up encryption for data at rest and in transit.

Compute resources. Request GPU instance quota increases early - cloud providers limit GPU access by default, and quota increases can take days.

Storage. Set up S3 buckets for training data, model artifacts, and pipeline outputs. Configure lifecycle policies for cost optimization.

Workload Migration

For each workload:

  1. Containerize if not already containerized. Docker containers provide portability and consistency between on-premise and cloud.
  2. Test in cloud environment with a subset of data. Verify that outputs match on-premise results.
  3. Run parallel in both environments for a validation period. Compare results to confirm equivalence.
  4. Cut over to cloud when confidence is established. Keep on-premise capability available for rollback.
  5. Decommission on-premise resources after a stability period.

Validation

Model equivalence. Train the same model with the same data on-premise and in the cloud. Compare metrics (loss, accuracy, evaluation scores). Small differences are expected due to floating-point non-determinism; large differences indicate a migration bug.

Performance validation. Verify that latency, throughput, and availability meet SLAs in the cloud environment under realistic load.

Cost validation. Compare actual cloud costs against estimates. Adjust instance types and auto-scaling policies based on real usage data.

Optimization Phase

After migration, optimize for cloud:

Spot instances for training. Use spot instances for fault-tolerant training jobs. Savings of 60-90% compared to on-demand.

Auto-scaling for inference. Configure auto-scaling for model serving endpoints. Scale down during low traffic, scale up during peaks.

Managed services. Replace self-managed infrastructure with managed services where appropriate. SageMaker manages the infrastructure lifecycle for training and inference.

Storage tiering. Move infrequently accessed data to S3 Infrequent Access or Glacier. Keep only active training data in standard S3.

Right-sizing. After observing real usage patterns, adjust instance types and sizes. Most initial deployments are over-provisioned.

Cloud migration for AI workloads is a phased journey, not a one-time event. Start with low-risk workloads, build expertise and confidence, then migrate progressively more critical workloads. The operational benefits (on-demand scaling, managed services, reduced maintenance) justify the migration effort, but only if the migration is planned and executed carefully.