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Wrapper

As explained in Machine Learning System Design

A software component that encapsulates another component behind a controlled interface while coordinating surrounding infrastructure behavior.

A wrapper puts an HTTP interface around a model and coordinates the infrastructure around it. It manages structured JSON requests and responses while allowing the model and its surrounding services to evolve through separate release processes. section 13.5.1

A practical wrapper coordinates:

  • Request handling through an HTTP API handler and prediction extraction through a model API. section 13.5.1
  • Infrastructure releases with integration testing, backward-compatibility checks, deployment procedures, and system-health monitoring. section 13.5.1
  • Independent model releases driven by new data, changing patterns, or modeling improvements. section 13.5.1
GET /predictions?query=<query_string>&parameters=<parameters>&version=
<version>
&limit=<limit>&request_id=<request_id>&sku=<sku>&entity_id=<entity_id>
&group=<group_type>

The request listing shows the wrapper’s query parameters, including versioning, limits, request identifiers, products, entities, and groups. section 13.5.1

{
    "predictions": [
        {
            "sku": <sku_id>,
            "demand": <demand>,
            "entity": <entity_id>,
            "period": <time_period_for_demand>,
        },
        ...
    ]
}

The response listing shows predictions returned as structured records containing SKU, demand, entity, and period fields. section 13.5.1

How Machine Learning System Design covers it

How deep it goes
Explains API responsibilities and release practices, but stops short of implementation code or internal wrapper mechanics.
Examples
Uses a prediction endpoint with query parameters and a JSON response containing SKU, demand, entity, and period.
What you can do after
Operate/configure a prediction service with separated wrapper and model release processes.
How it connects
Connects HTTP API design with model APIs, data drift, fallback systems, monitoring, and software deployment.

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This explainer was generated by Manning's knowledge graph from the text of Machine Learning System Design. The section links point to the parts of the book it draws on.