Abstract: Artificial Intelligence is already helping many businesses become more competitive, but how do you move machine learning (ML) models efficiently from research to production? We believe it is imperative to plan for production from day one—both by using a discipline process and by choosing the right tools. Fortunately, we don’t have to build from scratch, as Machine Learning Engineers we can adapt many of the best practices from the DevOps playbook and apply them to the ML workflow.
In this session, we explain some of the ML development best practices we have developed from working in the trenches. The core of our approach comes down to a philosophy of engineering discipline around “being the Navy, and not pirates.” Wherever possible, we explain how to reduce the incidental complexity in ML development by using appropriate tooling and process. We deep dive into certain best practices like using Docker on day one, continuous integration for ML, experiment tracking using MLFlow, and experimenting at scale in the cloud.
* Background (10 mins)
* Key Lessons
** Use Docker on Day One (25 min)
** Use a Structured ML Software Workflow (25 min)
** Downstream Containerization Benefits (15 min)
* Conclusion / Q&A (15 min)
Bio: Alexander Ng is a Senior Data Engineer at Manifold, an artificial intelligence engineering services firm with offices in Boston and Silicon Valley. Prior to Manifold, Alex served as both a Sales Engineering Tech Lead and a DevOps Tech Lead for Kyruus, a startup that built SaaS products for enterprise healthcare organizations. Alex got his start as a Software Systems Engineer at the MITRE Corporation and the Naval Undersea Warfare Center in Newport, RI. His recent projects at the intersection of systems and machine learning continue to combine a deep understanding of the entire development lifecycle with cutting-edge tools and techniques. Alex earned his Bachelor of Science degree in Electrical Engineering from Boston University, and is an AWS Certified Solutions Architect.