Automation and Development Effort in Continuous AI Development: A Practitioners’ Survey

The widespread adoption of AI-enabled systems and their required continuous development and deployment (MLOps) sparks research interest due to the added intricacy of automatically handling data, code, and the model itself. A better understanding of the stages for the continuous development of AI, namely Data Handling, Model Learning, Software Development, and System Operations, and the respective tasks can help to optimize and improve their effectiveness.Thus, this paper explores the degree of automation, development effort, importance, utilization of computing resources, and factors contributing to automation throughout these stages and tasks. We conducted a questionnaire-based global survey to explore these topics by analyzing 150 responses from experienced AI, data, and MLOps engineers.The results determined that the stage System Operations is mainly automated. Whereas several tasks from the other three stages (e.g., data cleaning, data quality assurance, model design, model improvement, and system level quality assurance) are more often partially automated than automated, and documentation-related tasks are mostly not automated or developed. Participants required the highest development effort for the stage Data Handling. Furthermore, the study reveals a negative correlation between automation and the perceived development effort, whereas the importance of the tasks does not seem to affect automation. 93% of participants consider the availability of computing resources, with model training, data transformation, and data cleaning ranked as the most resource-intensive tasks.