Trustworthy and robust deployment of AI applications requires adherence to a range of AI engineering best practices. But, while professionals already have access to frameworks for deploying AI, case studies and developer surveys have found that many deployments do not follow best practices.

We hypothesize that the adoption of AI deployment best practices can be improved by finding less complex framework designs that combine easy of use with built-in support for best practices. To investigate this hypothesis, we applied a design science approach to develop a new framework and evaluate its ease of use and best practice support.

The initial design focusses on the domain of natural language processing (NLP), but with generalisation in mind. To assess applicability and generalisability, we conducted interviews with ten practitioners. We also assessed best practice coverage.

We found that our framework helps implement 33 best practices through an accessible interface. These target the transition from prototype to production phase in the AI development lifecycle. Feedback from professional data scientists and software engineers showed that ease of use and functionality are equally important in deciding to adopt deployment technologies, and the proposed framework was rated positively in both dimensions.