ai-ml-developer-workflow

# AI-ML developer workflow Join us and watch this training session focused on the Qualcomm Linux AI Developer Workflow. It is tailored for developers and professionals in the IoT sector who are keen to enhance their understanding and skills in deploying on-device AI solutions. The AI/ML developer workflow on Qualcomm Linux has two major steps: | Step 1

Compile and optimize a model |

Compile and optimize the model from the third-party AI
framework to efficiently run on Qualcomm hardware. For
example, a Tensorflow model can be exported to a TFLite
model.

Optionally, quantize, fine-tune performance, and accuracy
using hardware-specific customizations.

| | --- | --- | | Step 2

Build an application to use the optimized model to run on
device inference |

Integrate the AI model into the use case pipeline.

Cross-compile the application to generate an executable
binary using dependent libraries.

| Important - Ensure that the host computer uses Ubuntu 22.04. - The commands in this document are compatible with Qualcomm Linux 1.3. Verify your Qualcomm Linux release version by running the commands described in the [Dev Kit Quick Start guide](https://docs.qualcomm.com/bundle/publicresource/topics/80-70017-253/getting_started.html) If your release version is not 1.3, [update your software](https://docs.qualcomm.com/bundle/publicresource/topics/80-70017-253/set_up_the_device.html#panel-0-VWJ1bnR1tab$update-software). - Sample applications and AI procedures in this document are compatible with the [supported versions](https://docs.qualcomm.com/bundle/publicresource/topics/80-70017-51/introduction.html#supported-component-versions). Ensure you download the matching SDKs to your host computer before starting AI/ML development. Last Published: Jan 21, 2026 [Next Topic Overview](https://docs.qualcomm.com/bundle/publicresource/80-70017-15B/topics/overview.md)