How deeply does Machine Learning System Design cover api model?

Specifies responsibilities, JSON boundaries, release cadence, and layering, but stops short of implementation details.

Api model

As explained in Machine Learning System Design

A model API is a programmatic interface that exposes machine-learning model operations such as generating embeddings and scoring candidates.

A model API exposes model operations to the surrounding system. In demand forecasting, it extracts predictions directly from the model; in image search, it extracts query embeddings and scores retrieved candidates. section 13.5.1 section 13.5.2

Keep transport and business responsibilities separate. An HTTP handler parses JSON, forwards requests, wraps responses, and handles errors; the model API performs model-specific work. Search systems can place vector-database retrieval and result ranking in adjacent APIs rather than inside the model API. section 13.5.2

A practical model-API design distinguishes:

Prediction extraction for forecasting services, with responses containing items such as SKU, demand, entity, and period. section 13.5.1
Embedding extraction and candidate scoring for search, followed by filtering and relevance ranking in neighboring services. section 13.5.2
Independent release rhythms: infrastructure wrappers require integration and compatibility testing, while model updates may be less frequent. section 13.5.1 section 13.5.2

GET /predictions?query=<query_string>&parameters=<parameters>&version=
<version>
&limit=<limit>&request_id=<request_id>&sku=<sku>&entity_id=<entity_id>
&group=<group_type>

Inspect a versioned demand-prediction endpoint with query, parameters, limit, request, SKU, entity, and grouping fields. section 13.5.1

```
GET /search?query=<query_string>&filters=<filters>&version=<version>
&limit=<limit>&request_id=<request_id>
```

Inspect a versioned search endpoint that accepts a query and filters and returns ranked result identifiers and scores. section 13.5.2

How Machine Learning System Design covers it

liveBook manning.com

Read Machine Learning System Design on liveBook Get the book at manning.com

Also explained in

liveBook manning.com

ASP.NET Core in Action, 3eAn API model is the data representation returned by an API action for serialization and transmission to a client.
liveBook manning.com

Functional Design and Architecture: Examples in HaskellAn API model is a data representation designed for communication across an application programming interface rather than for internal domain behavior.

Compare how 3 books explain api model →

Related concepts

domain modelproduces · A domain model is a software representation of the concepts, entities, relationships, rules, and behavior of a problem or business domain.
apiuses · An application programming interface (API) is a defined set of operations and conventions through which software components access functionality or exchange data.
contractuses · An API contract is a specification of the operations, data, and behavioral expectations that govern interactions between software components.
endpointuses · An endpoint is a network-accessible address or interface through which a service exposes a resource or operation and handles requests and responses.
data transfer objectequivalent to · A data transfer object is a data-carrying structure that conveys defined information between software components or across application boundaries without encapsulating substantial domain behavior.
Formatterused by · A formatter is a software component that converts or structures data, records, or messages according to a specified representation format.

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.