How I got selected in LFX :)
About LFX
LFX Mentorship (previously known as Community Bridge) is a platform developed by the Linux Foundation, which accelerates the adoption, innovation, and sustainability of open-source software.
LFX Mentorship is actively used by the Cloud Native Computing Foundation as a mentorship platform across the CNCF projects
Program Schedule
2021 — Fall Term — In Progress
2021 — Summer Term — Completed
2021 — Spring Term — Completed
The program occurs for 3 times in a year
How to Apply
Candidates are required to provide cover letters (Statement of Purpose) while applying, so mentors may review them. In addition, mentors may also interview the candidates to understand their level of qualification before making the final decision.
In addition, a Resume and a permission letter are also required to apply for this program.
Pro tip: Start contributing to the project and interact with the mentors prior to the mentorship begins.
About my Project:
Open Horizon is a platform for managing the service software lifecycle of containerized workloads and related machine learning assets. It enables autonomous management of applications deployed to distributed webscale fleets of edge computing nodes and devices without requiring on-premise administrators.
Edge computing brings computation and data storage closer to where data is created by people, places, and things. Open Horizon simplifies the job of getting the right applications and machine learning onto the right compute devices, and keeps those applications running and updated. It also enables the autonomous management of more than 10,000 edge devices simultaneously — that’s 20 times as many endpoints as in traditional solutions.
With Open Horizon, you have the ability to:
- Add new capabilities to a single-purpose device
- Enable your device to use other services (both nearby and cloud-based) to enhance its existing capabilities
- Automate the hands-free management of workload lifecycle on the device
- Automatically deploy applications to all devices where policies match and an agreement is negotiated
Once Open Horizon is installed and configured, you can manage the work it performs without needing physical access to the devices. For example, you could take a Raspberry Pi Zero W device — the size of a pack of gum — and have it function as an offline home assistant one day, and then re-task it to monitor household power usage the next. Or, you could have a smart camera pointed at a street count the number of people walking by one day, and deploy new code and models to have it identify the make and model of vehicles passing by the next.
Whether thinking about component solutions that could be assembled for a complete Cloud-to-Edge workload management implementation, or projects that would work together to solve a specific use case, Open Horizon is designed to work together with other solutions in an open, standardized approach.
Formerly known as “Blue Horizon,” Open Horizon is contributed to LF Edge by IBM. Open Horizon joined LF Edge in April 2020 and is currently a Stage 2 “Growth” project.
Cross-LF Edge Collaboration
Open Horizon is already working with several other LF Edge projects including EdgeX Foundry, Akraino Edge Stack, Home Edge, Fledge and Project EVE.
The project is in discussions with several Akraino Blueprint Families on contributing to their blueprints as a portion of their solutions in the areas of workload management on edge devices and delivery of assets in an ML deployment pipeline.
Open Horizon is also planning to collaborate with:
- Fledge to deliver and manage their solution and to synchronize any needed ML assets with their origin.
- Home Edge to synchronize ML assets from the cloud to the home gateway.
- Project EVE using Open Horizon as a control plane for EVE workloads in a POC.
How I started:
I hoped onto the slack channel of Open Horizon and gave my intro. I interacted with the mentors of Open Horizon. I asked every doubt and thanks to my mentors for clearing those doubts.
Also I started exploring the project one month prior to the mentorship begun. A key factor that helped me in my selection.
Next Steps:
I would be now contributing to amazing projects of LF- Edge. See you till the next time :)
Connect with me on Linkedin.
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