The rise of machine learning (ML) and its embedding in software-intensive systems has drastically changed the engineering of such systems. Traditionally, software engineering focuses on manually created artifacts, such as source code, and the process of creating them, as well as best practices for integrating them, i.e., software architectures. In contrast, the development of ML artifacts, i.e., ML models, comes from data science and focuses on the ML models and their training data. However, to deliver value to end users, these ML models must be integrated with traditional software components, often forming complex topologies. In fact, ML-enabled software can easily incorporate many different ML models. While the challenges and practices of building ML-enabled systems have been studied, little is known about the characteristics of real-world ML-enabled systems, beyond isolated examples. Properly embedding ML models in systems so that they can be easily maintained or reused is far from trivial. To improve development processes and architectures for ML-enabled systems, we need to improve our empirical understanding of these systems. We present the first large-scale study of real-world open-source ML-enabled software systems, covering over 2,928 systems on GitHub. We classified and analyzed them to determine their characteristics, as well as their practices for reusing ML models and related code, and the architecture of these systems. Practitioners and researchers benefit from insights into practices for embedding and integrating ML models, bringing data science and software engineering closer together.