Effective service deployment and orchestration will be essential to accommodate user workloads with diverse requirements in cloud-native beyond 5G Radio Access Networks (RAN). Orchestration will have to take into account individual service quality requirements, latency constraints, and dependencies, while leveraging unique characteristics of dominant workloads, such as machine learning (ML) models. In this work, we address the orchestration of ML-based services, considering users that request application services that rely on network services, such as localization, positioning, etc. Each service is composed of functions, at potentially different quality levels. The objective is to maximize the network operator’s revenue by determining service deployment, quality selection and computational resource allocation. The resulting problem is a mixed-integer non-convex problem, which we show is NP-hard. We provide sufficient conditions for the problem to be submodular, and for the general case we propose JADES, which relies on linear relaxation and convexification to decompose the problem into two subproblems, which are solved iteratively until convergence, followed by dependent randomized rounding. Our evaluation based on synthetic workloads shows that JADES outperforms baselines in terms of operator revenue and computational efficiency.