Exploring Trade-Offs in MLOps Adoption

Machine Learning Operations (MLOps) play a crucial role in the success of data science projects in companies. However, despite its obvious benefits, several companies struggle to adopt MLOps practices and face difficulty in deciding how to deploy and evolve ML models. To gain a deeper understanding of these challenges, we conduct a multi-case study involving nine practitioners from seven companies. Based on our empirical results, we identify the key trade-offs we see companies make when adopting MLOps. We categorise these trade-offs into four concerns of the BAPO model: Business, Architecture, Process, and Organisation. Finally, we provide suggestions to mitigate the identified trade-offs. By identifying and detailing these trade-offs and the implications of these, this research helps companies to ensure the successful adoption of MLOps.