Model Serving Migration Playbook
A five-phase migration of ML serving from bespoke endpoints to a standardized, autoscaling, observable platform. It baselines performance, pilots with parity checks, migrates the fleet via strangler-fig, then optimizes GPU cost.
Model Serving Migration Playbook
Many teams accumulate bespoke model-serving endpoints: one-off Flask apps, hand-rolled containers, and inconsistent autoscaling. This playbook migrates that sprawl onto a standardized serving platform with consistent packaging, autoscaling, and observability. It targets organizations consolidating inference workloads to cut cost and improve reliability.
Phase-by-Phase
Inventory and Baseline. Inventory every serving endpoint, its model, runtime, and traffic. Baseline latency and cost so you can prove parity later. Rank migration order by risk and value.
Target Platform Design. Select a serving runtime that fits your models and hardware. Design autoscaling, including scale-to-zero for spiky traffic. Define a packaging standard so every model ships the same way, hardened and reproducible.
Pilot Migration. Migrate a few representative models. Validate output parity against the legacy endpoints and tune throughput and batching. Use canary releases and the expand-and-contract pattern to migrate without downtime.
Fleet Migration. Migrate the remaining models using a strangler-fig approach, routing traffic incrementally. Decommission legacy endpoints once parity holds. Enforce the packaging and configuration standards across the fleet.
Optimize and Operate. Optimize GPU utilization through batching and right-sizing. Establish on-call and runbooks. Control cost with FinOps practices and capacity planning.
Team and Roles
An architect owns platform design and migration sequencing. Data engineers and backend engineers adapt model packaging. DevOps engineers build the pipeline. SREs own reliability and on-call.
Risks and Mitigations
- Serving parity drift between old and new endpoints; mitigate with side-by-side validation and golden test inputs.
- Latency regression under the new runtime; mitigate with load testing and tuning before cutover.
- Cost overrun from idle GPUs; mitigate with autoscaling, batching, and utilization tracking.
Success Criteria
Track p99 latency, GPU utilization, and serving cost per request. Success means equal or better latency at lower cost with higher utilization.
Tooling
Kubernetes hosts the serving fleet, with Docker and OCI images for packaging. Python wraps the models. Prometheus collects metrics and Istio manages traffic shifting during migration.