CREATE SE4AI is pleased to present AI Adoption in Telecom Networks as part of our Industry Talks webinar series.

Did you know that revenue generated from AI automation in telecom networks is expected to reach USD 200 billion by 2027? What are some of the current challenges Ericsson, a global leader in ICT solutions faces as they adopt new technologies in AI and ML in their products and services, and how are they overcoming them?

In his presentation, Karthikeyan Premkumar, Data Scientist at Ericsson used case studies to highlight several touchpoints involving the ML model development process, tools, data quality, ML Ops (monitoring and retraining) and deployment as part of model industrialization.

About the Speaker

Karthik Premkumar has 18 years of experience in the telecom industry. His expertise lies in industrializing AI/ML solutions, designing system and solution architectures in BSS, Cloud and infrastructure platforms for telecom networks.

In his current role as Data Scientist at Ericsson in Montreal, QC, Karthik Premkumar designs knowledge models and machine reasoning technologies for cognitive networks. He has published over 10 patents and papers on telecom analytics.

The webinar recording is available to view on our CREATE SE4AI YouTube channel.

On Friday, July 17 at 10 AM, CREATE SE4AI hosted another edition of Trainee Talks, featuring MASc candidate Kawsar Ahmed Bhuiyan, a member of the REALISE Lab at Concordia University under the supervision of Prof. Diego Elias Costa. Kawsar presented his work titled "Beyond Compliance: A Large-scale Study on the Completeness and Consistency of the GitHub SBOMs."

Abstract:

Modern software development relies heavily on open-source components. Reusing components accelerates innovation but increases exposure to supply-chain attacks exploiting known vulnerabilities. Software Bills of Materials (SBOMs) improve software supply chain transparency by enumerating components, their versions, and their provenance. GitHub, the largest open-source development hosting platform, now automatically generates SBOMs for repositories, providing valuable metadata for risk assessment.

During the talk, Kawsar presented the findings of a large-scale analysis of 10,000 GitHub repositories across ten programming language ecosystems, evaluating GitHub SBOMs against three other popular SBOM generators: Syft, Trivy, and the Microsoft SBOM Tool. The study found a lack of NTIA compliance in GitHub SBOMs, although core metadata was consistently present. It also showed that the availability of component version and license information depends heavily on the programming ecosystem. Compared with the other three tools, GitHub produced results similar to the Microsoft SBOM Tool and often outperformed Syft and Trivy in providing version and license information. The presentation concluded with a discussion of potential shortcomings of the GitHub SBOM Tool related to how different programming ecosystems manage their dependencies.

The video is now available to view on our CREATE SE4AI YouTube channel.