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NNStreamer 2.0 and Beyond Announcement: NNStreamer Releases version 2.0 NNStreamer released its version 2.0 last October. It is a set of GStreamer plugins supporting easier and more efficient adoption of neural network models for GStreamer developers, adopting neural network models and neural network developers managing neural network pipelines and their filters. There are a variety of improvements added in version 2.0. Through Edge-AI capabilities, users may connect independent and remote pipelines with NNStreamer-provided protocols designed for AI data streams. In other words, a lightweight IoT device may offload its AI workload to its neighbor devices with GStreamer/NNStreamer pipeline descriptions, and an AI service may easily publish its output data streams for other AI pipelines. NNStreamer as Open SourceNNStreamer is a project of the Linux Foundation AI & Data (LF AI & Data), and is currently in the incubation stage. It was contributed by Samsung last year after said project was open sourced in 2018. The LF AI & Data Foundation is an organization created to establish an ecosystem for open source innovation in the fields of AI, machine learning, and deep learning. Recently, it has announced its Principles for Trusted AI, which include Explainability, Transparency, Robustness, and 5 others. As seen below [1], there are various open source projects aside from the NNStreamer project. Additionally, there are also other open source projects relating to data stream processing.Achievement of CII Best Practices "passing" badgeAs part of the effort to advance the project from Incubation to the Graduate level this year, NNStreamer achieved a Core Infrastructure Initiative Best Practice (CII BP) Badge [2], as well as documented and structured their open governance processes. A Linux Foundation (LF) CII BP Badge is a way for Open Source projects to show that they are following best practices. The badges let others quickly assess which projects are following best practices and hence are more likely to produce higher-quality secure software. It also helps open source projects find areas where they can improve. A CII BP Badge shows that an open source project is implementing a large number of good practices to keep the project sustainable, addressing vulnerabilities when found, and is helping improve that security. There are three badge levels: passing, silver, and gold. Each level requires that an open source project meet a set of criteria; and for silver and gold this includes meeting the previous level. The “passing” level captures what well-run open source projects typically already do, and has 67 criteria grouped into six categories such as quality, security, etc. Other Improvements in the 2.0 ReleaseIn addition to the improvements in NNStreamer’s Edge AI capabilities, the security vulnerabilities discovered using the LGTM tool were resolved and improved in the NNStreamer 2.0 release. This is more meaningful as the code enhancement is done by a contributor who, while is a new participant in the NNStreamer project, collaborated and contributed to it. Interview with NNStreamer Project PioneersWe are pleased to announce the NNStreamer 2.0 release with code maturity and quality enhancement above. To celebrate it more, we arranged an interview with Maintainer MyungJoo Ham, Committers Jaeyun Jung and Taras Drozdovsky. Kindly read through the interview. First, we congratulated them on the release of version 2.0 of NNStreamer, and asked them to tell us the background story of the project’s birth. “In 2018, we were working on a project on autonomous driving vehicles. In this project, we have seen the vehicle as a high-end and real-time on-device AI system that is required to execute a lot of neural networks interconnected with each other and to fetch high-bandwidth data streams from various sensors, which has caused enough pain to consider writing a new software framework or approach for the whole system. Another major pain usually neglected by many developers was that, with a lot of developers involved, there are a large number of changes in the software system that change relations between components, which affect the overall architecture of system, causing a lot of effort and cost. A small group of developers was tasked to mitigate this issue by applying a new architecture or software framework. After a series of discussions, research, and prototyping, we have determined that a pipe-and-filter architecture is suitable for such systems and complex on-device AI systems in general and we have decided to use GStreamer as the basis of such systems. This was the beginning of the project, NNStreamer, although the NNStreamer group was soon detached from autonomous driving project and started working for different on-device AI systems of conventional embedded devices. It has started with a single developer (the founder) and soon garnered attention of more developers. Within a few months, the NNStreamer group has grown to have about 10 developers and declared to be an open source project. The first commercialization has occurred within a year after starting the project, and it has soon become the standard machine learning framework of Tizen for various consumer electronic devices.” MyungJoo Ham answered. Then, we asked, “What was the most impressive thing during the past three years when contributor strength has increased and the project has grown, as shown in the graph below? [3]” “It’s definitely contributions from other affiliations, including other companies, students, and hobbyists. It’s especially impressive to see other affiliations (e.g., NXP Semiconductors and Collabora) to contribute to the project and hearing how they are going to use NNStreamer for their own projects.” he said about collaboration. “Supporting multiple platforms became important as each project required a different environment to run the NNStreamer application. So, we had improved our project implementing one code-set to support various platforms and neural network frameworks.” a committer, Jaeyun Jung added this impressive moment too. We can find more details about the multiple platforms that he talked about in GitHub NNStreamer. You can also track all the related issues including the new release if you want. We then talked about Edge AI on the recent release, by asking them on what they think the role of Edge Intelligence in the future, as well as on how NNStreamer will be developed in relation to Edge Intelligence. “I view Edge AI or Among-Device AI as a distributed intelligence system with physically enclosed environments (e.g., in-home or in-building), where personal data may be used by intelligence systems while such data are not exposed out of the enclosed environments. As becoming among-device AI systems, any device, regardless of its computing power or sensor components, may provide consistent intelligence services as if every device interfacing users has high computing power and various sensor components by sharing such resources connected in the enclosed environments. We are extending the capability of NNStreamer so that it can not only provide on-device AI capability but also provide among-device AI capability by implementing inter-pipeline stream connections that are easy-to-use and efficient-to-transmit. Another set of capabilities is to deploy and publish AI services atomically so that other among-device AI entities may subscribe. We will also provide API sets that can be used by among-device AI applications, either based on NNStreamer or not, to connect with NNStreamer pipelines as AI services for among-device AI capability. All the related components will be available as open source as it has always been. With the capability to make non-NNStreamer software connect with NNStreamer pipelines as AI services will further enable different systems to be connected for the given among-device AI systems. In conclusion, by allowing any party to publish AI services for NNStreamer’s among-device AI (a.k.a. Edge AI) systems and to subscribe to such AI services even if the corresponding devices do not have NNStreamer running, we expect that we can create an intelligent IoT device ecosystem that is open to anyone and any system. Ultimately, we would like to provide such among-device AI streaming mechanisms to IoT standards including Matter and SmartThings so that more devices and more people may enjoy the capability of NNStreamer.” a maintainer, MyungJoo Ham explaining the vision of the project. Jaeyun Jung also shared his opinions on expanding the AI ecosystem. “Suppose you create AI services using NNStreamer. If several models need to be linked or the models continue to be changed during development, application developers sometimes have to modify the pipeline or change the code whenever these changes occur. Application developers have some requirements to minimize code modification even if these changes occur. Therefore, we will provide an ML API that application developers can use to facilitate code maintenance regardless of changes in the model or pipeline as the work of changing the model will be registered as a separate service.” he said Finally, it is more meaningful that Taras Drozdovsky, who is participating in several open source projects as a security expert to acquire this CII BP Badge, participated in the NNStreamer project to collaborate and contribute. He shared his experience in participating as a new committer. “Like every developer, before the moment, you create your first commit in a project, I go the way from the first familiarity with the product to implementing a good idea. Therefore, any Open Source project should create an atmosphere that will not leave any developer indifferent, inspire him to improve it, and answer the main questions: What kind of project is this? What is it for? How and where can it be used? When I visited the GitHub NNStreamer project and saw the “Applications” part [4], all the answers were obtained! I understood that I wanted to put a piece of myself to create something incredible! Summing up this part, I can say that the structure and description of the project make an attractive force, and I want to do something to improve it. The next step is more detailed familiarization with the project build and launch of examples. And here, everything was at the highest level. For a few hours, I was able to build a project and run a few examples. A miracle happened in front of my eyes! I once again made sure that quality documentation was significant. I started to think about what contribution I could make, and I analyzed the project using my experience in security. I did not find serious vulnerabilities but found that it can make the product more protected and efficient. Opening the documentation again at the “Contributing” part and following the instructions, I created my first commit and pull request and began to wait for the maintainers’ reaction. The discussion with them was rapid and high-quality (they are real professionals). So the first step was done! And now I am sure that I will not stop! Result: The CII Best Practices “Passing” badge was obtained! And now, every developer seeing it can be sure that he will face high-quality documentation and safe source code.” he vividly said. Your contribution is always welcomed! Looking at the proportion of the total number of commits, the NNStreamer project is mainly operated by Samsung developers. However, others such as NXP also have participated and collaborated. In addition, LF TSC regular meetings and webinars are held, and external outreach activities are steadily carried out. We invite you to visit the GitHub where NNStreamer and its subprojects are developed. Anyone can participate in its development and contribute to the project according to open source governance. NNStreamer Key Links- Website: http://nnstreamer.ai/- Github: https://github.com/nnstreamer/nnstreamer- Wiki: https://wiki.lfai.foundation/display/NNSTREAM/NNStreamer+Home- Mail Lists - NNStreamer-Announce: https://lists.lfai.foundation/g/nnstreamer-announce - NNStreamer-Technical-Discuss: https://lists.lfai.foundation/g/nnstreamer-technical-discuss - NNStreamer-TSC: https://lists.lfai.foundation/g/nnstreamer-tscReference[1]https://landscape.lfai.foundation/card-mode[2]https://bestpractices.coreinfrastructure.org/en/projects/4401[3]https://insights.lfx.linuxfoundation.org/projects/lfai%2Fnnstreamer/dashboard;quicktime=time_filter_3Y[4]https://github.com/nnstreamer/nnstreamer* Original Contents : https://research.samsung.com/blog/NNStreamer-2-0-and-Beyond November 26, 2021
O-RAN SC: 'Dawn' of RAN Intelligence What is O-RAN SC?O-RAN Software Community (O-RAN SC) was formed in April 2019 under Linux Foundation, sponsored by the O-RAN Alliance. Focus of O-RAN SC is to deliver open source enabling modular, open, intelligent, efficient, and agile disaggregated Radio Access Network (RAN).“The O-RAN Software Community is a collaboration between the O-RAN Alliance and Linux Foundation with the mission to support the creation of software for the Radio Access Network (RAN). The RAN is the next challenge for the open source community. The O-RAN SC plans to leverage other LF network projects, while addressing the challenges in performance, scale, and 3GPP alignment.” [1]RAN Intelligent Controller Applications (RICAPP), Near-Realtime RAN Intelligent Controller (RIC), Non-RealTime RAN Intelligent Controller (NONRTRIC), Operations and Maintenance (OAM), O-RAN Central Unit (OCU), O-RAN Distributed Unit High Layers (ODUHIGH), O-RAN Distributed Unit Low Layers (ODULOW), Infrastructure (INF), Simulations (SIM), Integration and Testing (INT) are key sub-projects that constitute the O-RAN SC.In August 2021, O-RAN Alliance announced the fourth release of O-RAN SC : “Dawn”. 60+ developers from about 10 companies worked together tirelessly to meet the release deadline for Dawn release with significant contributions to all the sub-projects [2]. Dawn release includes major enhancement to SMO (Service Management and Orchestration), ODUHIGH, SIM, and OAM projects to support “closed-loop automation” usecase. This usecase demonstrates the end-to-end automation of configuration and monitoring for any network events. Also there was a strong focus to harden the platform by complying with the Linux Foundation Core Infrastructure Initiative (CII) Best Practices Badging Program, which has emerged as one of popular and authorized barometers to guarantee the best practice “Open Source” in terms of its code review process, governance, security management, DevOps and so on. Most of the sub-projects completed the CII badging criteria to meet basic “passing” grade in this release. Overall, this release has been a stepping-stone to bring RAN innovation and openness on a secure platform and has also laid the framework for rApps (running on top of non-RT RIC platform) and xApps (running on top of near-RT RIC platform) development. Details for the D release can be found at O-RAN SC site [3].What is Samsung’s contribution in O-RAN SC?Samsung has been actively contributing into the O-RAN SC since 2019 with significant contributions to the RIC and RICAPP projects. Samsung contributors also hold a committer position in seven repositories (e2mgr[4], e2[5], a1[6], nodeb-rnib[7], hw-go[8], hw-python[9] and libe2ap [10]). Subhash Kumar Singh from Samsung Research India Bangalore is one of the top contributors (overall 12th position) in O-RAN SC and is holding the committer role for few key repositories related to RIC platform.Figure. The number of commits by organizations (counted since 21/01/01 to 21/08/25)Dr. Sunghyun Choi, Corporate SVP and Head of Advanced Communications Research Center, Samsung Research, Samsung Electronics, quoted in the official O-RAN SC press release about the focus and vision of Samsung’s contribution [11]."Making the RAN intelligent and context-aware is critically important for network optimization and automation. The focus of Samsung's O-RAN SC contribution has been towards enhancing the A1-Enrichment Information (A1-EI) & xApp Framework. Overall, we believe that strengthening the O-RAN AI/ML Framework will play a big role in making operators achieve proactive closed-loop optimization and automation of RAN operations."The near-RT RIC platform is a very critical component in O-RAN architecture for bringing intelligence to RAN. On top of the near-RT RIC platform, developers can build their own applications xApp to optimize network parameters by utilizing the data and the machine-learning (ML) framework provided by the platform. The accuracy of an ML algorithm is largely dependent on the number of data points that are available. Based on the use case, xApps might need information that is outside the purview of the RAN but has significant impact on the overall network performance, e.g., weather conditions. In this release, we focussed to implement A1-EI interface at near-RT RIC as per the specification provided by O-RAN Alliance. This feature will enable xApps to consume enrichment information from external source, which when used in conjunction with the data from the network should improve the accuracy of the prediction algorithm and in turn promote the usage of predictive closed-loop automation for RAN optimization. Additionally, the python libraries related to the xApp framework were improved to support an ML framework that can be used by xApp developers to apply ML Algorithms to RAN control.Further in this release, O2 interface implemented as per the WG6 use case “Deploy xApp in near-RT RIC” in O-RAN Orchestration Use Cases v2.0. Now, xApp initiates registration on its startup. We improved the python framework to support the registration on startup. To make the encoding and decoding of E2AP v1.0 messages more simplified by introducing message structure and wrappers in libe2ap repository. As our focus we have also improved KPIMON xApp to implement E2SM KPM 2.0.3, which is recently approved by O-RAN Alliance. New E2SM model will include new KPI parameter and expose those details to RIC to bring new innovation and ideas. We are also focussed to promote O-RAN SC as developer friendly by bringing reference xApps (Hw-go and Hw-python). xApp developers can refer to it in order to get familiarized with the features of xApp framework and start building new apps for RAN-related decision.Our developers are paving path towards enabling RAN intelligence with consistent contributions to the O-RAN SC RIC & RICAPP projects . Following contributors from Samsung have been very active in the O-RAN SC:- Subhash Kumar Singh (Senior Chief Engineer at Samsung R&D Institute India-Bangalore)- Naman Gupta (Senior Engineer at Samsung R&D Institute India-Bangalore)- Heewon Park (Engineer at Samsung Research)- YoungCheol Jang (Engineer at Samsung Research)- JinWei Fan (Senior Professional at Samsung R&D Institute China-Beijing)- Jeongyeob Oak (Staff Engineer at Samsung Electronics Networks Business)- Timothy Ebido (Staff Engineer at Samsung Research)While the O-RAN SC near-RT RIC platform has AI/ML capabilities, the realisation of a complete AI/ML workflow (as defined in the O-RAN Alliance WG2) would require additional components for the model training such as ML designer and ML training host. It also requires ML model serving frameworks to deploy inference service and inference optimization to reduce latency with efficient resource utilization. The Advance Solution Team at Samsung Research in collaboration with Samsung Research India Bangalore is working on building this end-to-end workflow by utilizing various open source projectsWhat are the next step?The E Release of O-RAN SC is currently in the planning phase and scheduled for December 2021. In this release, we can expect to see new xApps, usecases, and more mature platform to lead industry towards open, intelligent, secure, and fully interoperable RAN. On the end-to-end AI/ML framework front, we plan to demonstrate the capabilities of the framework with practical usecases that can help operators realise the true potential of predictive closed-loop automation.Reference[1] https://o-ran-sc.org/[2] https://insights.lfx.linuxfoundation.org/projects/o-ran-f/dashboard[3] https://wiki.o-ran-sc.org/pages/viewpage.action?pageId=20878658[4] https://gerrit.o-ran-sc.org/r/admin/repos/ric-plt/e2mgr[5] https://gerrit.o-ran-sc.org/r/admin/repos/ric-plt/e2[6] https://gerrit.o-ran-sc.org/r/admin/repos/ric-plt/a1[7] https://gerrit.o-ran-sc.org/r/admin/repos/ric-plt/nodeb-rnib[8] https://gerrit.o-ran-sc.org/r/admin/repos/ric-app/hw-go[9] https://gerrit.o-ran-sc.org/r/admin/repos/ric-app/hw-python[10] https://gerrit.o-ran-sc.org/r/admin/repos/ric-plt/libe2ap[11] https://static1.squarespace.com/static/5ad774cce74940d7115044b0/t/611a53ea2436a96c6c7f3a01/1629115370586/O-RAN.EC.August-16-2021-PR.v01.00.pdf* Original Contents : https://research.samsung.com/blog/O-RAN-SC-Dawn-of-RAN-Intelligence#none September 1, 2021
Samsung Research opens CredData on github Samsung Research opens CredData(https://github.com/Samsung/CredData) on github. CredData (Credential Dataset) is a set of files including credentials in open source projects. Unprotected credentials that are shared through code repositories are leading to undesirable incidents such as leakage of personal information. There are several Open Source projects detecting these credentials, but there is no reference dataset to evaluate the tools and very few tools can detect 'password' type. CredData is reference dataset which includes suspicious lines with manual review results and more information such as credential types including 'password' type for each suspicious line. The information that can be used to predict which Open Source projects are included is minimized and actual credentials are obfuscated. CredData can be used to develop new tools or improve the performance of existing tools. Furthermore, Samsung Research also released the benchmark result of the CredData which includes a new tool to be released in October. With the benchmark result, users can choose a proper tool among open source credential scanning tools according to their use case. Details you can find, in here : https://github.com/Samsung/CredData Samsung Research will continue to transparently open vulnerability-related activities. August 18, 2021
Open-Source Project for MPEG-5 EVC (Essential Video Coding) The COVID-19 outbreak has changed our lives and relationships over the past year. We now have no other choice but to communicate more often through the Internet. One of the key technologies for talking via the Internet is, definitely, video compression (coding) technology. However, a very old codec, H.264/AVC, is still the primary technology used in video conference applications, whose standardization was finalized about 20 years ago. Experts had undoubtedly developed a more efficient video codec called HEVC, but it is not dominantly available yet because of its complex license policy or computational complexity issues for video conferences.In June 2021, Samsung Research released an open-source video encoder (XEVE: eXtra-fast Essential Video Encoder, https://github.com/mpeg5/xeve) and decoder (XEVD: eXtra-fast Essential Video Decoder, https://github.com/mpeg5/xevd), which are software implementations of the state-of-the-art video coding standard, MPEG-5 Essential Video Coding (EVC), providing better visual quality for video calls and alleviating the expensive royalty for video codecs.The EVC standard completed by the ISO/IEC Moving Picture Experts Group (MPEG) in April 2020 defines two profiles: (1) the Baseline profile, which is royalty-free as it only consists of technologies that are more than 20 years old, and (2) the Main profile, which contains enhanced tools to improve the compression efficiency. At an equivalent level of visual quality, the Baseline and Main profiles save bit rate by approximately 40% compared to the H.264/AVC and HEVC standards, respectively. For your reference, HEVC saves approximately 40% of the bit rate compared to H.264/AVC.The main purpose of our source code release is to make the MPEG-5 EVC standard more widely and easily used in the industry. For example, the high-performance, commercial-ready, open-source encoder XEVE can be directly deployed to over-the-top (OTT) services without additional development and enable high-quality encoding of video contents. XEVD, a lightweight, open-source decoder, can be directly deployed on embedded devices such as smartphones to play video streams from OTT services. Moreover, XEVE and XEVD can provide improved visual quality and cost-efficient delivery for real-time video conference services, and we believe these services will continue to be popular even after the pandemic.In addition to this effort, Samsung has been striving to adopt the Baseline profile of EVC into the WebRTC standard for real-time video communication on web browsers and mobile applications. Its integration to the WebRTC standard shall prompt better experiences on virtual video communication at a lower cost.* Original Contents : https://research.samsung.com/news/Open-Source-Project-for-MPEG-5-EVC-Essential-Video-Coding July 23, 2021
Engineering Lead at Samsung Research Wins Multiple Top Awards in Robotics Community Samsung Research America’s Open-Source Robotics, Engineering Lead Steve Macenski was awarded by the global Robot Operating System (ROS) community top awards at the 2021 ROS Developer’s Day conference. This is the second year in a row Samsung Research has received awards from the robotics community.Steve Macenski was awarded, for the second consecutive year, the Top Developer Award for his on-going contributions to ROS and ROS2. He is the Technical Steering Committee (TSC) representative for Samsung and leads the community Navigation Working Group. As a core leader in ROS, he focuses on building highly reliable mobile robotics technology, community engagement, and mentorship. This award highlights his continued technical achievements in the field of robotics, including mapping, path planning, control, and architectural innovations. It also speaks to the success of his leadership in building a vibrant community. Steve’s initiatives have resulted in a community containing hundreds of developers, mentoring opportunities, and hundreds of contributions to high impact projects.Further, the community project, Nav2 (formerly known as Navigation2) was awarded the Top Software Award. This project is led by Steve Macenski of Samsung Research America with close collaboration with Alexey Merzlyakov of Samsung Research Russia. This project aims to provide the highest quality mobile robot navigation solution in the world. Today, it supports Ackermann, Differential Drive, Omnidirectional, and Legged robots with a wide range of sensing capabilities. In the last year, innovations in filters, path planning, control, and behavior trees have been made, which are enabling the next generation mobile robots. It is being used by dozens of companies, researchers, and hobbyists all over the world.Samsung Research’s Open Source Group is a leader in the next generation of robotics technology. They focus on developing the missing technologies holding back new and innovative applications of robotics. Further, this work is made to be well documented with guides and tutorials such that they can be easily integrated into any project. This focus on education and community has earned a strong reputation for Samsung in the ROS community. “[Steve’s] work for educational purposes and attitude in promoting robotics are very valuable and remarkable.” said Markus Buchholz, Principle Software Engineer at Canrig Robotics, “Frankly speaking and open minded [people] … help others to grow and live better.”* Original Contents : https://research.samsung.com/news/Engineering-Lead-at-Samsung-Research-Wins-Multiple-Top-Awards-in-Robotics-Community July 23, 2021
Samsung Electronics Adopts International Open Source Standard Samsung Electronics today announced the adoption of OpenChain ISO (International Organization for Standardization) / IEC (International Electrotechnical Commission) 5230:2020, the first international standard for open source compliance. The company joins a growing community of companies in the field of consumer electronics, automotive, cloud computing and telecommunications in using this standard to manage supply chains. The OpenChain Project began in 2016 as an initiative of the Linux Foundation, a non-profit organization in the United States. It aims to establish an effective and consistent open source compliance system. The OpenChain Project was adopted as an ISO / IEC international standard in 2020. The project evaluates each company’s open source compliance capabilities and grants certification if they met the criteria. “Samsung Electronics leads the adoption and use of open source technology to create innovative products,” said Seungbeom Choi, Executive Vice President and Head of R&D Strategy Team at Samsung Electronics. “Underlying this are organizations and processes specifically established to safely and efficiently utilize open source. Our adoption of OpenChain ISO 5230 reflects our ongoing commitment to excellence in our field. Samsung Electronics is expected to enhance its credibility by receiving recognition for its capacity to utilize open source through this certification. We look forward to collaborating with other industry leaders around this standard.” “Open source provides an unmatched platform for innovation,” said Shane Coughlan, General Manager of OpenChain. “To get the most out of this platform, the key is to have effective, efficient processes. OpenChain ISO 5230 delivers this for license compliance activities. We are delighted to welcome Samsung Electronics to our community of conformance. Their leadership in this area will help accelerate our mission throughout the supply chain.” Since 2012, Samsung Research, Samsung Electronics’ advanced R&D hub that leads the development of future technologies for its consumer product business, has been conducting various activities for safe and efficient use of open source with specialized organizations and systems. The company has established a global integrated open source policy with global research institutes, and established a system to identify open source licenses and comply with obligations at the stage of development from software planning to implementation and verification, and have operated online / offline education programs to enhance the understanding of developers.* Original Contents : https://news.samsung.com/global/samsung-electronics-adopts-international-open-source-standard July 16, 2021
[Upcoming event] SOSCON Global India Virtual Open Source Conference (5G, IoT and Edge Computing) :Date: 3rd July 2021Time 8.30 am to 2 pm (IST), 12:00 pm to 5:30 pm(KST)Link to Register : http://bit.ly/ComSoc_July3 Fast emerging technologies such as 5G, IoT, and Edge Computing are changing the landscape of the technology industry and bringing significant value to end consumers. At the same time, open-source platforms are rapidly evolving and playing an important role in meeting the agile demands of the industry. Join us to learn from the industry-leading experts on the value, innovation, and benefits of deploying emerging technologies using open source platforms.You will also learn how to develop and deploy Kubernetes Operator, LF Home Edge and O-RAN SC XApp through developer sessions. June 17, 2021