ecommerce_ops · workflow
Shopify builds Merlin, an internal ML platform for distributed training and batch inference at scale
Shopify needed to redesign their ML platform to handle diverse, often conflicting requirements across internal use cases (fraud detection, revenue predictions) and external use cases (product categorization, recommendation systems), enabling data scientists to move faster from prototype to production.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Create Merlin Project
A data scientist starts a new ML use case by creating a Merlin Project, specifying required packages and dependencies.
Tools used
MerlinRayRay TrainRay TuneRay ActorPoolJupyterHubKubernetesTensorFlowDockerDatadogSplunkSpark
Outcome
Merlin is empowering Shopify with scalability, fast iteration, and flexibility, validated by onboarding the product categorization model which required large-scale computation and complex ML flows.
Grounding & classification
Source type: technical build writeup
30 fields verified against source quotes.
document classificationforecastingfraud detectionrecommendation systemproduct catalognamed customerproduction runtime claimedtools describedworkflow describedsoftwarecycle time reductionemployee productivitytechnical build writeupecommerce opsextract classify route