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Accelerate edge AI development with SiMa.ai Edgematic with a seamless AWS integration
- SiMa.ai and AWS offer a seamless integration for deploying ML models at the edge, enhancing efficiency and scalability for various use cases including safety monitoring in real-time.
- The solution combines SiMa.ai's Edgematic platform with Amazon SageMaker AI for training and deploying optimized ML models on SiMa's MLSoC hardware, ensuring compatibility and cost-effectiveness.
- By retraining and quantizing a model with SageMaker AI and Palette software suite, accurate detection of individuals and safety equipment for compliance and safety is achieved directly on edge devices with low latency.
- The workflow involves ML training, exporting, edge evaluation, and deployment, simplifying the process of deploying AI applications on the edge without constant cloud connectivity.
- Key steps include creating a custom image for SageMaker JupyterLab, training the model, performing graph surgery, quantization, and compilation to prepare the model for edge deployment.
- SiMa.ai Edgematic allows for seamless execution of the optimized model with essential plugins like UDP sync and video encoders for real-world AI applications on the edge.
- The process also involves transitioning from cloud-based model fine-tuning to Edgematic for building edge applications, enabling detection of specific categories like PPE equipment in real-time.
- The post explains detailed steps on training object detection models, optimizing them for edge deployment, and building applications without custom coding to enhance workplace safety.
- Users can further experience the streamlined workflow using SiMa.ai Palette on SageMaker JupyterLab to achieve high performance, low latency, and energy efficiency in their ML applications.
- To avoid ongoing costs, users are advised to clean up resources on SiMa.ai Edgematic and AWS by shutting down resources after completing the application deployment and testing.
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