Everything open source
May 16–17, 2016: Training & Tutorials
May 18–19, 2016: Conference
Austin, TX

How-to: Your first contribution

Michelle Casbon (Google)
5:10pm–5:50pm Wednesday, 05/18/2016
Open Source 101
Location: Ballroom C Level: Intermediate
Average rating: ****.
(4.38, 8 ratings)

Prerequisite knowledge

Familiarity with basic programming and standard developer tools would be helpful but is not necessary.

Description

For years, I had admired open source projects from afar. 2015 was the first time I made a meaningful contribution to an open source project. Prior to that, the open source community seemed like a big, scary world of superstar coders with brains bigger than mine. I eventually realized that committers are normal people too, after few serendipitous events (like actually meeting one).

If you can relate and are interested in getting involved but don’t know where to start, then this is the session for you. I’ll break down the process into simple, easy-to-follow steps that give you an idea of what the experience is like, in the context of my own journey. You’ll hear about how I chose a project, what inspired me to get started, where I struggled, where I turned to for help, what I would do differently, and how it changed my career.

My experience joining the open source community showed me that it is a much less intimidating place than I expected. Meeting several key contributors and committers along the way helped me understand that anyone can get involved, including you!

Photo of Michelle Casbon

Michelle Casbon

Google

Michelle Casbon is a senior engineer on the Google Cloud Platform developer relations team, where she focuses on open source contributions and community engagement for machine learning and big data tools. Michelle’s development experience spans more than a decade and has primarily focused on multilingual natural language processing, system architecture and integration, and continuous delivery pipelines for machine learning applications. Previously, she was a senior engineer and director of data science at several San Francisco-based startups, building and shipping machine learning products on distributed platforms using both AWS and GCP. She especially loves working with open source projects and is a contributor to Kubeflow. Michelle holds a master’s degree from the University of Cambridge.