jbottum
commented
10 months ago Terry stands as a distinguished multi-year contributor to the Kubeflow community, leaving a lasting impact through critical contributions. As a dedicated code contributor, Terry’s expertise extends to comprehensive documentation efforts. Terry's efforts include a recently published book on Distributed Machine Learning Patterns, which he authored and I recommend. Displaying leadership qualities, Terry has taken on the role of Argo lead, showcasing a profound commitment to the success of that project and to Kubeflow. Additionally, as a leader in both communities, Terry exemplifies organizational and coordination skills, while actively mentoring to nurture talent within the community. Terry has shared valuable insights as a speaker at a variety of conferences and venues, contributing significantly to the dissemination of knowledge. Notably, Terry's unwavering commitment to the Kubeflow community persists through changes in employment. Based in the US, Terry brings a crucial perspective to the collaborative and global efforts of the Kubeflow ecosystem. These qualifications make Terry a great candidate for the Kubeflow Steering Committee.
andreyvelich
akgraner
Yuan (Terry) Tang
LinkedIn: https://www.linkedin.com/in/terrytangyuan
Github: https://github.com/terrytangyuan
Q: Why do you think you would be a good candidate for the Kubeflow Steering Committee?
Kubeflow enters the next critical phase by joining CNCF as an incubating project. As a current project lead and maintainer of Argo, I hope I can bring my experience with Argo (from incubating to graduated project within CNCF) to help with the transition of Kubeflow to CNCF and its future journey towards successful graduation.
In the meantime, my 6 years of involvement in the Kubeflow community as the tech lead and co-chair of WG Training gives me a holistic overview of our community and understanding of areas of improvements that we can address together. I will make my best decisions that benefit the community based on my first-hand experience in maintaining and advocating for the project.
Kubeflow is an umbrella of projects that offer great toolkits for ML applications in the cloud-native world with great community momentum and velocity. It’s both exciting and challenging as it involves so many areas in ML lifecycle and projects in the ecosystem and AI is gaining huge traction these days. I have held various leadership and maintainer positions in projects (e.g. Argo, TensorFlow, XGBoost) and organizations in the ecosystem (CNCF, JMLR, JOSS). I hope I can leverage my relationship with them to accelerate the growth of Kubeflow adoption.
Q: How long have you been involved in the Kubeflow Community? What projects have you been actively involved with?
6 years. My work in the Kubeflow community focuses on the distributed training operator. I’ve been leading the development and maintenance of MPI Operator, XGBoost Operator, and MXNet Operator. I also lead the efforts to establish Kubeflow Common, which provides common and standardized APIs and libraries shared by other Kubeflow operator repositories. All these efforts are now merged into the unified training operator.
Besides the training operators, I’ve also been actively contributing to other components such as Kubeflow Pipelines and Katib through code, documentation, design discussions, etc.
Q: How long have you been involved in open-source?
9 years.
Q: Is there anything else you would like the community to know about you that you believe would help eligible voters make there decision?
I’d like to share some of my other non-code contributions/involvement related to Kubeflow over the years.
Q: Links to any external sites (projects, hobbies, etc) that you would like to share that would help people make a decision about.
Summary of my open source contributions: https://github.com/sponsors/terrytangyuan Complete list of open source projects I’ve been involved in: https://terrytangyuan.github.io/projects/ List of public talks: https://github.com/terrytangyuan/public-talks