What happens when you have a bunch of data scientists, a bunch of new and old projects, a big grab-bag of runtime environments, and you need to get all those humans and all that code access to GPUs? Come see how the ML Eng team at Mailchimp wrestled first with connecting abstract containerized processes to very-not-abstract hardware, then scaled that process across tons of humans and projects. We’ll talk through the technical how-to with Docker, Nvidia, and Kubernetes, but all good ML Engineers know that wrangling the tech is only half the battle and the human factors can be the trickiest part.

3 Key Takeaways:

  • An overview of the call stack from container, orchestration framework, OS, and all the way down to real GPU hardware
  • How ML Eng at Mailchimp provides GPU-compatible dev environments for many different projects and data scientists
  • An experienced take on how to balance data scientist’s human needs against heavy system optimization (spoiler alert: favor the humans)

Emily Curtin

Senior Machine Learning Engineer | Mailchimp

Emily May Curtin is a Senior Machine Learning Engineer at Mailchimp, which is definitely what she thought she’d be doing back when she went to film school. Emily works closely with data scientists to get their math out there to the real world through massive-scale processes, stable deployments, and robust live services. Truthfully, she’d rather be at her easel painting hurricanes and UFOs. Emily lives (and paints) in her hometown of Atlanta, GA, the best city in the world, with her husband Ryan who’s a pretty cool guy.

Emily Curtin