Machine Learning as a Software Engineering Discipline with Dillon Erb - #404
Published:Aug 27, 2020 19:23
•1 min read
•Practical AI
Analysis
This article summarizes a podcast episode of Practical AI featuring Dillon Erb, CEO of Paperspace. The discussion focuses on the challenges of building and scaling repeatable machine learning workflows. The core theme revolves around applying software engineering practices to machine learning, emphasizing reproducibility and addressing technical issues faced by ML teams. The article highlights Paperspace's experience in this area, from providing GPU resources to developing their Gradient service. The conversation likely delves into how established software engineering principles can be adapted to improve the efficiency and reliability of ML pipelines.
Key Takeaways
- •The article discusses the importance of applying software engineering principles to machine learning.
- •Reproducibility in production machine learning pipelines is a key topic.
- •The conversation covers technical issues ML teams face when scaling ML workflows.
Reference
“The article doesn't contain a direct quote, but the focus is on applying time-tested software engineering practices to machine learning workflows.”