  Radio access network (RAN) technologies continue to evolve, with Open RAN
gaining the most recent momentum. In the O-RAN specifications, the RAN
intelligent controllers (RICs) are software-defined orchestration and
automation functions for the intelligent management of RAN. This article
introduces principles for machine learning (ML), in particular, reinforcement
learning (RL) applications in the O-RAN stack. Furthermore, we review the
state-of-the-art research in wireless networks and cast it onto the RAN
framework and the hierarchy of the O-RAN architecture. We provide a taxonomy
for the challenges faced by ML/RL models throughout the development life-cycle:
from the system specification to production deployment (data acquisition, model
design, testing and management, etc.). To address the challenges, we integrate
a set of existing MLOps principles with unique characteristics when RL agents
are considered. This paper discusses a systematic model development, testing
and validation life-cycle, termed: RLOps. We discuss fundamental parts of
RLOps, which include: model specification, development, production environment
serving, operations monitoring and safety/security. Based on these principles,
we propose the best practices for RLOps to achieve an automated and
reproducible model development process. At last, a holistic data analytics
platform rooted in the O-RAN deployment is designed and implemented, aiming to
embrace and fulfil the aforementioned principles and best practices of RLOps.
