Although different team members have their own style of managing experiments and tracing their outcomes, it is recommended to adopt a common way of logging data; that is understood and accessible to all team members.
Sharing the outcomes within the team has several benefits for peer review, knowledge transfer and model assessment.
Several collaborative tools enable central logging of experimental results.
Whenever possible, it is recommended to use one of the tools available internally or externally (e.g. Sacred or W&B).
- 10 Best Practices for Deep Learning
- Principled Machine Learning: Practices and Tools for Efficient Collaboration