Automate AI Workflows with Qualcomm AI Runtime

🚀 Technical Briefing: This tutorial is part of our deep-dive series on Agentic Workflows at Gate of AI. For the full technical breakdown, interactive code sandbox, and the native Arabic translation, visit the original article here.

<span>Tutorial</span>
<span>Advanced</span>
<span>⏱ 45 min read</span>
<span>© Gate of AI 2026-07-07</span>

In this tutorial, you'll learn to automate AI workflows using Qualcomm AI Runtime, enhancing deployment efficiency and model performance.

Prerequisites

• Python 3.10 or later
• Qualcomm AI Runtime SDK
• Intermediate to Advanced Python programming skills
• Basic understanding of neural networks and AI model deployment

What We're Building

In this tutorial, we will build a sophisticated AI workflow automation system using the Qualcomm AI Runtime (QAIRT). This system will allow you to efficiently deploy AI models, manage inference tasks, and handle model updates with minimal downtime. The finished project will streamline the deployment process, reduce errors, and optimize resource usage.

The system leverages advanced features of QAIRT, such as model optimization, efficient resource allocation, and seamless integration with various AI frameworks. By the end of this tutorial, you will have a deep understanding of how to automate complex AI workflows, enabling scalable and efficient AI deployments in production environments.

Setup and Installation

To begin, we need to set up our development environment and install the necessary tools. This includes the Qualcomm AI Runtime SDK, which provides the tools and libraries required to build and optimize AI workflows.

pip install qairt-sdk

Next, configure your environment variables to include the necessary paths for the QAIRT SDK. This ensures that the SDK tools can be accessed from any directory in your terminal.

  
  
  .env file example


QAIRT_HOME=/path/to/qairt-sdk

PATH=$QAIRT_HOME/bin:$PATH

  

Make sure to replace /path/to/qairt-sdk with the actual path where the SDK is installed on your system.

Step 1: Preparing Your AI Model

The first step in automating your AI workflow is to prepare your AI model for deployment. This involves converting your model to a format that is compatible with the Qualcomm AI Runtime.

import torch

from torchvision import models

  
  
  Load a pre-trained model


model = models.resnet50(pretrained=True)

  
  
  Export the model to ONNX format


torch.onnx.export(model, 

                  torch.randn(1, 3, 224, 224), 

                  "resnet50.onnx", 

                  export_params=True, 

                  opset_version=11, 

                  do_constant_folding=True, 

                  input_names = ['input'], 

                  output_names = ['output'])

  

This code snippet demonstrates how to export a PyTorch model to the ONNX format, which is widely supported by AI deployment frameworks. The torch.onnx.export function converts the model and saves it as an ONNX file, making it ready for optimization and deployment.

Step 2: Optimizing the Model with Qualcomm AI Runtime

With your model in ONNX format, the next step is to optimize it using the Qualcomm AI Runtime. Optimization enhances performance by reducing model size and increasing inference speed.

from qairt_sdk import ModelOptimizer

  
  
  Initialize the model optimizer


optimizer = ModelOptimizer()

  
  
  Optimize the ONNX model


optimized_model_path = optimizer.optimize("resnet50.onnx", target_device="snapdragon")

print(f"Optimized model saved to {optimized_model_path}")

  

Here, we use the ModelOptimizer class from the QAIRT SDK to optimize the ONNX model for a specific target device, in this case, a Snapdragon processor. The optimized model is saved to a specified path, ready for deployment.

Step 3: Automating Deployment with QAIRT

Once your model is optimized, you can automate its deployment using the Qualcomm AI Runtime's deployment tools. This step involves setting up a deployment pipeline that handles model updates and scales with demand.

from qairt_sdk import DeploymentManager

  
  
  Initialize the deployment manager


deployment_manager = DeploymentManager()

  
  
  Deploy the optimized model


deployment_manager.deploy(optimized_model_path, service_name="image-classification-service")

print("Model deployed successfully as image-classification-service")

  

The DeploymentManager class facilitates the deployment of AI models as services. By specifying the service name, you can easily manage and update your model deployments, ensuring that your AI system remains responsive and up-to-date.

⚠️ Common Mistake: Ensure that your environment variables are correctly set up before running the optimization and deployment scripts. Incorrect paths can lead to errors during model conversion and deployment.

Testing Your Implementation

To verify that your AI workflow automation is working correctly, you can test the deployed service using sample input data. This helps ensure that your model is performing as expected in a production environment.

import requests

  
  
  Sample input data


input_data = {"input": [0.0] * 224 * 224 * 3}

  
  
  Send a request to the deployed service


response = requests.post("http://localhost:8000/predict", json=input_data)

  
  
  Check the response


print(response.json())

  

This test script sends a sample input to the deployed service and prints the response. The output should match your expectations based on the model's training, confirming that the deployment is successful.

What to Build Next

With your AI workflow automation in place, you can extend this tutorial by:

• Integrating additional models into your deployment pipeline for multi-model inference.
• Implementing a monitoring system to track model performance and resource usage in real-time.
• Exploring advanced deployment strategies, such as A/B testing and canary deployments, to optimize model performance.