Skip to main content
This page is the application-development step of the workflow. Once your model runs in a pipeline and its output is converted into usable metadata, you choose a development path — GStreamer, Python, C++, or the Coding Agent — validate the pipeline, and take it from bring-up to production. This workflow page keeps the decisions and the sequence in one place and links into the full QIM SDK documentation for the detailed API references, quickstarts, and sample pipelines you need along the way.
Step 4 of the application development workflow — the final step, where every model route converges.

​
Before you begin

Confirm the following before you start building. This page assumes the model integration from the earlier steps is already working.

​
Choose a development path

QIM SDK provides four ways to build an application, and all of them drive the same QIM SDK plugins. The pipeline you validate early therefore stays valid as you move to a higher-level application layer. Pick the path that matches the current maturity of your application.
New to the App Builders? Start with the App Builder introduction in the QIM SDK documentation for an overview of the programming models before you choose one.
Validate the AI pipeline before developing the application layer. This approach helps isolate model, inference, and post-processing issues early, making integration and debugging significantly easier.
1

Validate the model

Validate the model independently of the final application and confirm its expected input and output tensor formats. If the model still requires conversion, optimization, or quantization, complete those steps first in the model preparation workflow.
2

Select or create a post-processing module

Choose an existing qtimlpostprocess module that supports your model’s output format. If no suitable module exists, implement a custom post-processing module to convert raw inference outputs into QIM SDK-compatible metadata.
3

Verify the pipeline with GStreamer

Run the complete AI pipeline in GStreamer before writing application code. Validate preprocessing, inference, post-processing, labels, thresholds, and output rendering using a reference pipeline. This provides a fast feedback loop for troubleshooting and tuning.
4

Integrate into an application

Once the pipeline is validated, migrate it into a C++ App Builder or Python App Builder application if your deployment requires a structured application framework. The Coding Agent can help generate the application scaffold and integrate the validated pipeline.
5

Validate on the target platform

Test the application on the target Qualcomm platform using representative workloads, resolutions, frame rates, and deployment settings. Verify that performance and functionality meet production requirements before release. See the production readiness checklist.
Validate each layer independently. A working GStreamer pipeline significantly reduces the effort required to diagnose issues after application integration.
This AI Developer Workflow provides a guided path for building and deploying AI applications with QIM SDK. For detailed implementation guidance, including installation, plugin documentation, App Builder APIs, sample pipelines, and reference applications, refer to the QIM SDK documentation. Use this workflow to determine what to do next, and the QIM SDK documentation to learn how to do it.

​
Quickstart references

Use the QIM SDK quickstart that matches your selected development path. The GStreamer quickstart is recommended for first-time validation of an AI model pipeline.

GStreamer App Builder

Validate the pipeline on the command line, then scale up.

Python App Builder

Prototype and orchestrate the application in Python.

C++ App Builder

Build the production application with QIM SDK C++ APIs.

Not sure which to pick? Start with the App Builder introduction

Compare the GStreamer, Python, C++, and Coding Agent programming models in the QIM SDK documentation before you commit to one.