Several architecture frameworks for software, systems, and enterprises have been proposed in the literature. They identified various stakeholders and defined architecture viewpoints and views to frame and address stakeholder concerns. However, the stakeholders with data science and Machine Learning (ML) related concerns, such as data scientists and data engineers, are yet to be included in existing architecture frameworks. Therefore, they failed to address the architecture viewpoints and views responsive to the concerns of the data science community. In this paper, we address this gap by establishing the architecture frameworks adapted to meet the requirements of modern applications and organizations where ML artifacts are both prevalent and crucial. In particular, we focus on ML-enabled Cyber-Physical Systems (CPSs) and propose two sets of merit criteria for their efficient development and performance assessment, namely the criteria for evaluating and benchmarking ML-enabled CPSs and the criteria for evaluation and benchmarking of the tools intended to support users through the modeling and development pipeline. This study deploys multiple empirical and qualitative research methods based on literature review and survey instruments, including expert interviews and an online questionnaire. We collect, analyze, and integrate the opinions of 77 experts from over 25 organizations in 10 countries to devise and validate the proposed framework.