Running Effective Machine Learning Teams: Common Issues, Challenges and Solutions

Despite significant progress in the deep learning space, implementing scalable machine learning pipelines still presents critical challenges. Even the best teams struggle with effective iteration of models, reproducible work, and maintaining institutional knowledge when teammates leave.

Gideon Mendels describes how leaning on traditional software engineering practices and tools contribute to these blockers and impact production model performance. These insights come from previous experience as a researcher at Columbia University, building deep learning models at Google, as well as experiences with Comet.ml, which enables data scientists to automatically track their datasets, code changes, and experimentation history.

Gideon then outlines key opportunities to improve machine learning development through emerging software tools and algorithmic advancements, including reproducible experiment management, automated hyperparameter optimization, and model visibility.