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      • Computer vision
        • Image classification
        • Object detection
          • Object detection with bounding boxes
          • Detect objects with centroid (FOMO)
        • Visual anomaly detection
        • Visual regression
      • Audio
        • Sound recognition
        • Keyword spotting
      • Time-series
        • Motion recognition + anomaly detection
        • Regression + anomaly detection
        • HR/HRV
        • Environmental (Sensor fusion)
    • Data
      • Data ingestion
        • Collecting image data from the Studio
        • Collecting image data with your mobile phone
        • Collecting image data with the OpenMV Cam H7 Plus
        • Using the Edge Impulse Python SDK to upload and download data
        • Trigger connected board data sampling
        • Ingest multi-labeled data using the API
      • Synthetic data
        • Generate audio datasets using Eleven Labs
        • Generate image datasets using Dall-E
        • Generate keyword spotting datasets using Google TTS
        • Generate physics simulation datasets using PyBullet
        • Generate timeseries data with MATLAB
      • Labeling
        • Label audio data using your existing models
        • Label image data using GPT-4o
      • Edge Impulse Datasets
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      • Building custom processing blocks
      • Sensor fusion using embeddings
    • Machine learning
      • Classification with multiple 2D input features
      • Visualize neural networks decisions with Grad-CAM
      • Sensor fusion using embeddings
      • FOMO self-attention
    • Inferencing & post-processing
      • Count objects using FOMO
      • Continuous audio sampling
      • Multi-impulse (C++)
      • Multi-impulse (Python)
    • Lifecycle management
      • CI/CD with GitHub Actions
      • Data aquisition from S3 object store - Golioth on AI
      • OTA model updates
        • with Arduino IDE (for ESP32)
        • with Arduino IoT Cloud
        • with Blues Wireless
        • with Docker on Allxon
        • with Docker on Balena
        • with Docker on NVIDIA Jetson
        • with Espressif IDF
        • with Nordic Thingy53 and the Edge Impulse app
        • with Particle Workbench
        • with Zephyr on Golioth
    • API examples
      • Customize the EON Tuner
      • Ingest multi-labeled data using the API
      • Python API bindings example
      • Running jobs using the API
      • Trigger connected board data sampling
    • Python SDK examples
      • Using the Edge Impulse Python SDK to run EON Tuner
      • Using the Edge Impulse Python SDK to upload and download data
      • Using the Edge Impulse Python SDK with Hugging Face
      • Using the Edge Impulse Python SDK with SageMaker Studio
      • Using the Edge Impulse Python SDK with TensorFlow and Keras
      • Using the Edge Impulse Python SDK with Weights & Biases
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      • deployment-metadata.json
      • ei-metadata.json
      • ids.json
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      • On your Raspberry Pi Pico (RP2040) development board
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      • Image classification - Sony Spresense
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      • Keyword spotting - TI LaunchXL
      • Keyword spotting - Syntiant - RC Commands
      • Running NVIDIA TAO models on the Renesas RA8D1
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      • Nordic Semi Thingy:91
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  • Tips & Tricks
    • Combining impulses
    • Increasing model performance
    • Optimizing compute time
    • Inference performance metrics
  • Concepts
    • Glossary
    • Course: Edge AI Fundamentals
      • Introduction to edge AI
      • What is edge computing?
      • What is machine learning (ML)?
      • What is edge AI?
      • How to choose an edge AI device
      • Edge AI lifecycle
      • What is edge MLOps?
      • What is Edge Impulse?
      • Case study: Izoelektro smart grid monitoring
      • Test and certification
    • Data engineering
      • Audio feature extraction
      • Motion feature extraction
    • Machine learning
      • Data augmentation
      • Evaluation metrics
      • Neural networks
        • Layers
        • Activation functions
        • Loss functions
        • Optimizers
          • Learned optimizer (VeLO)
        • Epochs
      • On-device learning
    • What is embedded ML, anyway?
    • What is edge machine learning (edge ML)?
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  1. Concepts
  2. Course: Edge AI Fundamentals

What is Edge Impulse?

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Last updated 4 days ago

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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 edge MLOps 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 AI lifecycle

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.

Note that while we have some pre-compiled software for supported boards to help you get started, we offer a variety of ways to collect data. 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 AWS S3 bucket. From there, data can be fetched and transformed using custom blocks.

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 deployment options 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 web API. This allows you complete the MLOps loop by monitoring models and triggering new data collection, model training, and redeployment as needed.

Edge Impulse Studio

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.

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 custom processing block.

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 custom learning block or use the expert mode to modify the ML training code.

Once trained, the models can be tested using a holdout set or by connecting your device to ingest live data.

Finally, your full impulse can be deployed in a variety of formats, 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 EON Tuner to try various impulse configurations to determine the best combination of blocks.

As mentioned previously, you can script all aspects of Studio using the web API, which allows you to construct full MLOps pipelines.

Enterprise features

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.

You also gain access to an organization hub to easily monitor and maintain projects along with automated data pipelines, 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 health machine learning example design to see how data is captured, stored, loaded, and transformed from production servers using Edge Impulse tools.

Compare our plans and pricing or sign up for a free Enterprise Trial today.

Getting started

One of the fastest ways to try Edge Impulse is to follow this guided tour of creating your own keyword spotting model in 5 minutes or our computer vision walkthrough. No programming experience is required!

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:

  • For beginners

  • For ML practitioners

  • For embedded engineers

Quiz

Test your knowledge on Edge Impulse with the following quiz:

Edge Impulse for the edge AI lifecycle
Edge Impulse data acquisition
Edge Impulse impulse design
Edge Impulse processing block configuration
Edge Impulse learning block configuration
Edge Impulse testing
Edge Impulse deployment options
Edge Impulse EON Tuner
Edge Impulse automated data pipelines