New release of EdgeX Foundry open source IoT framework

July 15, 2019 //By Jean-Pierre Joosting
New release of EdgeX Foundry open source IoT framework
A project under the LF Edge umbrella organization within the Linux Foundation, the fourth release of EdgeX Foundry is now available. Designated the "Edinburgh" release, it aims to establish an open, interoperable framework for edge IoT computing independent of hardware, silicon, application cloud, or operating system.

Created collaboratively via a global ecosystem, the latest release of EdgeX Foundry is a key enabler of digital transformation for IoT use cases and is a platform for real-world applications both for developers and end users across many vertical markets. EdgeX community members have created a range of complementary products and services, including commercial support, training and customer pilot programs and plug-in enhancements for device connectivity, applications, data and system management and security.

Launched in April 2017, and now part of the LF Edge umbrella, EdgeX Foundry is an open source, loosely-coupled microservices framework that provides the choice to plug and play from a growing ecosystem of available third party offerings or to augment proprietary innovations. With a focus on the IoT Edge, EdgeX simplifies the process to design, develop and deploy systems across industrial, enterprise, and consumer applications.

The fourth release in the EdgeX roadmap, Edinburgh offers a stable API baseline for the standardization of IoT edge applications that future-proof IoT investments by fostering an ecosystem of interoperable microservice-based capabilities and decoupling investments in edge functionality in areas such as connectivity, security and management from any given backend application or cloud. The EdgeX framework is designed to facilitate the secure deployment and management of devices and applications at the edge to accelerate time-to-market and enable new data-based services and capabilities such as Artificial Intelligence (AI) and Machine Learning (ML).


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