The best practices and infrastructures for developing and maintaining machine learning (ML) enabled software systems are often reported by large and experienced data-driven organizations. However, little is known about the state of practice across other organizations. Using interviews, we investigated practices and tool-chains for ML-enabled systems from 16 organizations in various domains. Our study makes three broad observations related to data management practices, monitoring practices and automation practices in ML model training, and serving workflows. These have limited number of generic practices and tools applicable across organizations in different domains.