# Run machine learning use cases

Source: [https://docs.qualcomm.com/doc/80-70023-50/topic/machine-learning-use-cases.html](https://docs.qualcomm.com/doc/80-70023-50/topic/machine-learning-use-cases.html)

LiteRT and the Qualcomm Neural Processing SDK runtime are used for inference in the
        machine learning use cases.

Before you run the use cases, do the following:

- Complete the preconditions mentioned in [GStreamer command-line use cases](https://docs.qualcomm.com/doc/80-70023-50/topic/gstreamer-application-use-cases.html).
- Follow the [Prerequisites](https://docs.qualcomm.com/doc/80-70023-50/topic/download-model-and-label-files.html) to download the
                artifacts such as models, labels, and input files required to run the GStreamer
                command-line use cases.

Important: The AI procedures in this guide are compatible
            with Qualcomm AI Runtime SDK v2.41 and LiteRT (or TFLite) v2.16.1. Ensure that you
            download the matching SDKs to your host computer before starting AI/ML
            development.

- **[LiteRT use cases](https://docs.qualcomm.com/doc/80-70023-50/topic/tensorflow-lite-use-cases.html)**  

LiteRT is a set of tools that allows on-device machine learning. You can  run your         models on mobile, embedded, and edge devices. LiteRT use cases allow you to run use cases         for image classification, object detection, image segmentation, and pose         estimation.
- **[Qualcomm Neural Processing SDK use cases](https://docs.qualcomm.com/doc/80-70023-50/topic/qualcomm-neural-processing-sdk-use-cases.html)**  

Qualcomm Neural Processing SDK (formerly known as Qualcomm Snapdragon Neural         Processing Engine (SNPE)) is used to run deep neural networks for inference. The use cases         describe the image classification, object detection, and image segmentation scenarios using         different ML models.
- **[Custom Gstreamer pipeline use cases](https://docs.qualcomm.com/doc/80-70023-50/topic/custom-gstreamer-pipeline-use-cases.html)**  

Custom Gstreamer pipeline helps you design and implement tailored multimedia         processing work flows using the GStreamer framework. These pipelines give you full control         over media processing, analysis, and delivery. You can connect specific GStreamer elements         and leverage their properties to fine-tune performance, latency, and output         formats.

**Parent Topic:** [GStreamer command-line use cases](https://docs.qualcomm.com/doc/80-70023-50/topic/gstreamer-application-use-cases.html)

Last Published: Mar 27, 2026

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