Conclusion: Four Areas for Improving Collaboration on ML-Enabled System Development
Data scientists and software engineers are not the first to realize that interdisciplinary collaboration is challenging, but facilitating such collaboration has not been the focus of organizations developing ML-enabled systems. Our observations indicate that challenges to collaboration on such systems fall along three collaboration points: requirements and project planning, training data, and product-model integration. This post has highlighted our specific findings in these areas, but we see four broad areas for improving collaboration in the development of ML-enabled systems: Communication: To combat problems arising from miscommunication, we advocate ML literacy for software engineers and managers, and likewise software engineering literacy for data scientists.
Documentation: Practices for documenting model requirements, data expectations, and assured model qualities have yet to take root. Interface documentation already in use may provide a good starting point, but any approach must use a language understood by everyone involved in the development effort.
Engineering: Project managers should ensure sufficient engineering capabilities for both ML and non-ML components and foster product and operations thinking.
Process: The experimental, trial-and error process of ML model development does not naturally align with the traditional, more structured software process lifecycle. We advocate for further research on integrated process lifecycles for ML-enabled systems.
PS: This one is from months ago, but still interesting