AWS provides cloud computing services, including computing power, storage, and databases, as well as analytics, machine learning, and other tools, to enable businesses to operate and innovate more efficiently and cost-effectively.
AWS and Edge Impulse deliver powerful AI solutions for edge devices. Together, we provide a platform for developing and deploying machine learning models on resource-constrained hardware, with direct access to cloud-based infrastructure for training and deploying these models at scale.
From MCUs to GPUs, we help enterprise customers with ML solutions, edge to cloud:
Data collection: Edge Impulse provides tools for gathering and labeling data from sensors and other edge devices. This can be used to train machine learning models for specific use cases on the AWS cloud and AWS SageMaker.
Model development: Users can build and train ML models using Edge Impulse's web-based interface. These can be optimized for specific hardware and software configurations.
Model deployment: Once trained and optimized, a model can be deployed to edge devices using Edge Impulse's platform. This allows edge devices to perform real-time inference on locally generated data such as low-powered MCUs.
Cloud integration: Edge Impulse and AWS provide both embedded-edge and cloud-based infrastructure for model training and management. This can be particularly useful for training models on large datasets or for managing multiple edge devices.
Data analytics: Edge Impulse models can use AWS to store and analyze data generated by edge devices, allowing for insights and predictions to be generated at scale.
With Edge Impulse and AWS, developers can create sophisticated machine learning solutions that run on edge devices and leverage cloud-based infrastructure for enhanced scalability and data analytics.