Abstract: 

Open source is critical in advancing the machine learning (ML) industry. It allows a continuous innovation exchange, supports global collaboration, and better interoperability between different ML frameworks. With its benefit, open source software has been heavily integrated into the daily lives of ML practitioners today. Many popular algorithms, tools, and languages they rely on are often open source projects managed and contributed by numerous individuals. These individuals who contribute back to the community come with various skills and sizes to keep the project healthy for the whole ML community. In short, the people behind these open source projects are critical to the ML ecosystem. However, many ML space projects find it challenging to grow the community to take the project to the next level. Why is that, what impact will it have in the future, and what can we do to accelerate innovation as a community?

Join Anna in this session to learn about how open source and machine learning coexist and what it means to be part of the machine learning open source community. In addition, she'll walk through how to navigate open source space using a popular MLOps project Kubeflow as an example, and share tips on how to set yourself up for success for your future contribution.

Bio: 

Anna Jung is a Senior ML Open Source Engineer at VMware, contributing to various open source projects related to Machine Learning. She believes in the importance of giving back to the community and is passionate about increasing diversity in open source. When away from the keyboard, Anna is often at film festivals supporting independent filmmakers.