2 Find common ground
Explaining your development practices and seeing how they complement your data scientists’ efforts is important to creating frictionless collaboration and an experience that works for everyone. To that end:
Encourage frequent touchpoints. Setting up frequent and regular touchpoints is best to help ensure that the projects you work on together remain on track.
Respect boundaries. Data scientists may not want or need to know how you get applications into production. Although MLOps is a popular concept, some scientists prefer to email you their Jupyter Notebooks. Respect their interests and the ways they like to work, and they will reciprocate.
Share each other’s processes. Besides learning how data scientists work, share your processes and the tools you use in production, like Git, Tekton, or Kubernetes. In the spirit of open source, give them a peek into your processes.
Use a common platform for collaboration. Common cloud-native AI development platforms like Red Hat® OpenShift® Data Science support and encourage collaboration between you and your data science team. It democratizes the use of AI tools and allows teams to implement and accelerate intelligent application development.