Deploying Machine Learning Models at the Edge Using Amazon SageMaker and SiMa.ai Palette Edgematic
Co-Authors: Manuel Lopez Roldan (SiMa.ai) and Jason Westra (AWS)
Introduction to Edge Deployment in Machine Learning
Safety Applications in High-Risk Environments
Retraining and Optimizing ML Models for Edge Devices
Solution Overview: Integrating SiMa.ai Edgematic with SageMaker
Workflow Stages: From Training to Edge Deployment
Prerequisites for Successful Implementation
Creating a Custom Image for SageMaker JupyterLab
Step-by-Step Guide to Launching SageMaker JupyterLab
Training the Object Detection Model with SageMaker
Performing Model Optimization: Graph Surgery and Quantization
Deploying and Evaluating the Model with SiMa.ai Edgematic
Conclusion: Enhancing Edge AI Applications Efficiently
Additional Resources for Further Exploration
About the Authors: Expertise Behind the Solution
Accelerating Edge AI Deployment: A Seamless Integration of Amazon SageMaker and SiMa.ai
In an era where machine learning (ML) plays a pivotal role in various industries, deploying models at the edge is crucial for applications requiring real-time insights. Co-authored by Manuel Lopez Roldan from SiMa.ai and Jason Westra, a Senior Solutions Architect at AWS, this post delves into deploying optimized ML models at the edge using Amazon SageMaker and SiMa.ai’s Palette Edgematic platform.
The Need for Edge Deployment in Safety-Critical Environments
Deploying ML models to detect human presence and safety equipment in environments such as warehouses, construction sites, and manufacturing floors can significantly enhance safety. Often, cloud-based image recognition suffers from latency issues crucial for instantaneous safety alerts. By implementing object detection models optimized for SiMa.ai’s MLSoC, organizations can ensure real-time monitoring directly on edge devices, minimizing delay and enhancing safety compliance.
Overview of the Solution
This solution integrates SiMa.ai Edgematic with SageMaker JupyterLab to deploy the YOLOv7 model at the edge. YOLO (You Only Look Once) models are at the forefront of computer vision for object detection and image segmentation. The seamless, low-code pipeline provided by Edgematic minimizes the complexity of device management while ensuring AI applications run entirely on the edge—reducing latency, enhancing security, and keeping data in-house.
Workflow Breakdown
The solution comprises two primary stages:
-
ML Training and Exporting: Use SageMaker AI to train and validate your model. Optimize the model using SiMa.ai SDKs, code it for compatibility with SiMa.ai MLSoC hardware, and prepare it for edge deployment.
- ML Edge Evaluation and Deployment: Transfer your compiled model artifacts to Edgematic for deployment to the edge device. Validate its performance in real-time directly on the edge device.
Steps to Build Your Solution
Here’s how to get started:
- Create a Custom Image for SageMaker JupyterLab.
- Launch SageMaker JupyterLab with Your Custom Image.
- Train the Object Detection Model.
- Perform Graph Surgery, Quantization, and Compilation.
- Transfer the Edge Optimized Model to Evaluate Performance.
Prerequisites
Before embarking on this journey, confirm you have the following:
- An AWS account
- Familiarity with SageMaker and ML concepts
- SiMa.ai Developer Portal access
Setting Up and Launching SageMaker JupyterLab
Custom Image Creation
Using SageMaker AI, create a custom image that integrates with the SiMa.ai Palette APIs to build and optimize your edge model.
Launching JupyterLab
Once your custom image is configured, launch SageMaker JupyterLab. Select a GPU-based instance to efficiently train your model, ensuring you have sufficient resources to handle the workload.
Training the YOLOv7 Model
Utilize the SiMa.ai provided Jupyter notebook to fine-tune your model to recognize classes, using datasets that ensure precision and accuracy. Adjust settings to optimize for edge deployment:
dataset.export(dataset_type=fiftyone.types.YOLOv5Dataset, labels_path=path, classes=classes)Model Optimization via Graph Surgery
Graph surgery is employed to modify the YOLOv7 model’s ONNX format, enabling smoother deployment without losing its learning capabilities. Unlike pruning, which diminishes the model’s complexity, graph surgery restructures operations vital for aligning with target hardware.
Performing Quantization and Compilation
After graph surgery, quantize the model for efficiency at the edge, lowering power consumption while keeping performance intact. Following successful quantization, compile the model to create binary files executable on SiMa.ai’s MLSoC.
Transitioning to Edgematic
Once ready, log into the Edgematic platform to create a new project and transform your YOLOv7 model into a functional AI application. The drag-and-drop interface allows easy integration with essential plugins.
Running Your Application
After deploying, request an edge device and initiate the application. Within seconds, you’ll see detections in the video stream, enhancing workplace safety significantly.
Conclusion
This post illustrated the potential of Amazon SageMaker and SiMa.ai’s Palette Edgematic to simplify the retraining, optimization, and deployment of models at the edge. By leveraging these technologies, developers can create efficient, real-time monitoring systems that promote safety and compliance across various industries.
Accelerate Your Edge AI Journey
Experience firsthand how Palette Edgematic and SageMaker AI can streamline your ML workflow from cloud to edge. Get started today and help shape the future of edge AI.
Additional Resources
For further insights and detailed resources, feel free to explore the following links:
About the Authors
Manuel Lopez Roldan is a Product Manager at SiMa.ai, dedicated to improving usability and adoption of AI deployment tools. Jason Westra, a Senior Solutions Architect at AWS, specializes in aiding startups to innovate using Generative AI and ML.
Together, let’s accelerate the future of edge AI.