Running Effective Machine Learning Teams

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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.

Leaning only on traditional software engineering practices and tools contribute to these blockers and impact production model performance. Join Comet Co-founder/CEO, Gideon Mendelsfor a webinar on July 31 at 2pm ET as he shares his insights on running effective machine learning teams with data scientists and team leaders. These learnings come from his previous experiences as a machine learning researcher at Columbia University, building deep learning models at Google, as well as experiences with Comet, which enables data scientists and teams to build more reliable machine learning models for real-world applications by streamlining the machine learning model lifecycle.

During the webinar, Gideon will discuss:

  1. Key opportunities to improve machine learning development through emerging software tools
  2. Algorithmic advancements: reproducibility, automated hyperparameter optimization, and model visibility
  3. How to increase visibility and collaboration across the team

Register for the webinar