Get a data engineer’s perspective on how effective collaboration is vital at each stage of developing our machine learning (ML) platform.
When Adevinta started working on a Machine Learning (ML) platform, we decided to internalise the process and create a platform that accommodates our specific needs. Read the full Medium article to get a data engineer’s perspective on what we needed from an ML platform, why collaboration was vital and how our teams tackled the development process.
- The steps of the ML pipeline
- Issues around user experience
- Principles to describe how the ML platform and its development should work
- The advantages of making Kubeflow integration an iterative process
- The working practices we used in order to share the development of the platform