Creating Harmony Between Machine Learning Engineers and Researchers

Discover strategies for fostering collaboration between machine learning engineers and researchers in this insightful 45-minute conference talk by Luna Feng, Research Scientist at Thomson Reuters Center for AI and Cognitive Computing. Drawing from her experience in both roles, Feng explores the challenges faced by each group and offers practical solutions to enhance teamwork. Learn about common issues researchers encounter, such as code modularity, performance vs. scalability trade-offs, and result reproducibility. Gain insights into the difficulties engineers face when implementing research algorithms, including result verification and code integration. Explore valuable tips for improving communication, streamlining workflows, and bridging the gap between research and production environments. Enhance your understanding of best practices in version control, code profiling, and unified preprocessing techniques to create a more harmonious and efficient ML development process.