Introduction: Why REST APIs Matter for AI AppsIn modern AI applications, REST APIs are the connective tissue between models, services, and clients. They allow you to invoke machine learning models, exchange data, and orchestrate workflows across microservices. The REST standard remains popular because it’s language-agnostic, easy to debug, and scales well.## The Role of REST in AI Model Invocation### Direct Model CallsWhen your model is deployed as a REST service, clients can send HTTP requests with input payloads and receive predictions. This can be as simple as a POST with JSON data.### Orchestrating Multiple ServicesMany AI workflows require chaining several models and enrichment services. REST APIs make it straightforward to call these in sequence or in parallel.## Designing a Clean Data Flow for AI Systems### Input NormalizationBefore sending requests to an AI API, normalize incoming data: validate types, convert formats, and strip unnecessary fields.### Output StructuringDesign responses so downstream services can consume them without transformation. This could mean a consistent JSON schema or specific field naming.### Chaining API ResponsesFeed the output of one API into another. Use intermediate storage or in-memory processing depending on performance needs.## Example: Calling an AI Model via JuheAPILet’s look at an example using JuheAPI for currency exchange—imagine plugging this into an AI system that predicts market trends.- Base URL: https://hub.juheapi.com/
