EdgeML

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EdgeML is a machine learning library for developing ML algorithms tailored for edge devices. It focuses on creating models with a small footprint for storage, latency, and energy consumption.

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At a glance

Pricing

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Free tier

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API

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Skill level

Technical

About

What is EdgeML?

EdgeML is a specialized machine learning library designed to facilitate the development of ML algorithms specifically for edge devices. Its core focus is on optimizing models to have a minimal footprint in terms of storage requirements, prediction latency, and energy consumption. This makes EdgeML particularly well-suited for deployment on resource-constrained devices and sensors, which are commonly encountered in various Internet of Things (IoT) applications. The library was developed by Microsoft Research India.

Best used for

Developing and deploying machine learning models efficiently on resource-constrained edge devices and IoT sensors.

Common actions

Develop edge ML models

Optimize ML for IoT

Reduce model footprint

Improve device efficiency

Deploy ML on constrained hardware

Capabilities

Key features

Edge device ML
Small footprint models
Low latency
Energy efficient
IoT applications

Target Audience

Machine Learning EngineersIoT DevelopersEmbedded Systems EngineersResearchers

Integrations

Not yet documented

Pricing & Plans

unknown

Free

FAQs

Is EdgeML an open-source library, and what are its licensing terms?

EdgeML is an open-source project developed by Microsoft Research India. It is typically released under a permissive license like MIT, allowing for broad use and modification in both academic and commercial projects. Specific licensing details can be found in its GitHub repository.

What types of machine learning models does EdgeML support for optimization on edge devices?

EdgeML primarily focuses on optimizing traditional machine learning models such as decision trees, linear models, and shallow neural networks for edge deployment. It emphasizes techniques like quantization, pruning, and efficient data structures to achieve its small footprint and low latency goals.

What are the typical hardware requirements or constraints for devices compatible with EdgeML-optimized models?

EdgeML is designed for highly resource-constrained devices, including microcontrollers, FPGAs, and low-power embedded systems. Models optimized with EdgeML can run on devices with very limited RAM (e.g., a few kilobytes) and processing power, making them ideal for IoT sensors.

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