What is Edge Impulse?
Last updated
Last updated
Edge Impulse is the leading edge AI platform for collecting data, training models, and deploying them to your edge computing devices. It provides an end-to-end framework that easily plugs into your edge MLOps workflow.
Previously, we looked at and how it can be used to standardized your edge AI lifecycle. This time, we introduce Edge Impulse as a platform for building edge AI solutions and edge MLOps pipelines.
Edge Impulse helps with every step along the edge AI lifecycle, from collecting data, extracting features, designing machine learning (ML) models, training and testing those models, and deploying the models to end devices.
Edge Impulse easily plugs into other machine learning frameworks so that you can scale and customize your model or pipeline as needed.
Edge Impulse Studio is a web-based tool with a graphical interface to help you collect data, build an impulse, and deploy it to an end device.
Data can be stored, sorted, and labeled using the data acquisition tool.
From there, an impulse can be created that includes one or more feature extraction methods along with a machine learning model.
Once trained, the models can be tested using a holdout set or by connecting your device to ingest live data.
Edge Impulse has a number of enterprise features to help you build full edge ML pipelines and scale your deployments. First, you have access to faster performance and more training time to create larger and more complex models.
Even though Edge Impulse works well for beginners and students, it is highly extensible for experts and engineers alike. The following guides can help you get started depending on your background:
Test your knowledge on Edge Impulse with the following quiz:
Note that while we have some to help you get started, we offer a variety of ways to . In many cases, data collection requires customized software (and sometimes custom hardware). This data can easily be stored in a third-party location, such as an . From there, data can be and .
Deployment can also be tricky, as edge devices can vary in their processing power, operating system (or lack thereof), and supported languages. As a result, Edge Impulse offers a number of that you can build your application around. In most cases, these deployed options come as open-source libraries that make interacting with the models easy.
Finally, all aspects of Edge Impulse can be scripted using a . This allows you complete the by monitoring models and triggering new data collection, model training, and redeployment as needed.
A number of off-the-shelf feature extraction methods can be used and modified to suit the needs of your particular project. You can also design your own feature extraction method using a .
Next, you can train a machine learning model (including classification, regression, or anomaly detection) using a learning block. A number of pre-made learning blocks can be used, but you can also create your own or use the to modify the ML training code.
Finally, your full impulse can be , including a C++ library, Linux process (controlled via Python, Node.js, Go, C++, and others), Docker container, WebAssembly executable, or a pre-built firmware for supported hardware.
Edge Impulse includes advanced features like the autoML tool known as to try various impulse configurations to determine the best combination of blocks.
As mentioned previously, you can script all aspects of Studio using the , which allows you to construct full MLOps pipelines.
You also gain access to an to easily monitor and maintain projects along with , which allow you to configure and run transformation blocks in sequence to extract, transform, and load (ETL) data from a variety of sources.
You can look through this to see how data is captured, stored, loaded, and transformed from production servers using Edge Impulse tools.
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One of the fastest ways to try Edge Impulse is to follow this guided tour of . No programming experience is required!
