  Context: Machine learning (ML)-enabled systems are being increasingly adopted
by companies aiming to enhance their products and operational processes.
Objective: This paper aims to deliver a comprehensive overview of the current
status quo of engineering ML-enabled systems and lay the foundation to steer
practically relevant and problem-driven academic research. Method: We conducted
an international survey to collect insights from practitioners on the current
practices and problems in engineering ML-enabled systems. We received 188
complete responses from 25 countries. We conducted quantitative statistical
analyses on contemporary practices using bootstrapping with confidence
intervals and qualitative analyses on the reported problems using open and
axial coding procedures. Results: Our survey results reinforce and extend
existing empirical evidence on engineering ML-enabled systems, providing
additional insights into typical ML-enabled systems project contexts, the
perceived relevance and complexity of ML life cycle phases, and current
practices related to problem understanding, model deployment, and model
monitoring. Furthermore, the qualitative analysis provides a detailed map of
the problems practitioners face within each ML life cycle phase and the
problems causing overall project failure. Conclusions: The results contribute
to a better understanding of the status quo and problems in practical
environments. We advocate for the further adaptation and dissemination of
software engineering practices to enhance the engineering of ML-enabled
systems.
