The rise of the Internet of Things (IoT) has led to an exponential increase in data generated by connected devices. Machine Learning (ML) has emerged as a powerful tool to analyze these data and enable intelligent IoT applications. However, developing and managing ML applications in the decentralized Cloud-to-Things continuum is extremely complex. This paper proposes Zenoh-Flow, a dataflow programming framework that supports the implementation of End-to-End (E2E) ML pipelines in a fully decentralized manner and abstracted from communication aspects. Thus, it simplifies the development and upgrade process of the next-generation ML-powered applications in the IoT domain. The proposed framework was demonstrated using a real-world use case, and the results showcased a significant improvement in overall performance and network usage compared to the original implementation. Additionally, other of its inherent benefits are a significant step towards developing efficient and scalable ML applications in the decentralized IoT ecosystem.