## 5 Unified Architecture for Machine Learning in Future Networks  
In order to facilitate the integration of ML capabilities into the future network architecture, the ITU-T Focus Group on ML for future networks including 5G (FG-ML5G) has developed a unified architectural framework for both ML functionalities and network functions. The following aspects of the architectural requirements have been considered in the design of this ML architecture. The architecture should be able to support various ML techniques/models and correlate the data in different formats obtained from heterogeneous resources. The architecture should be agnostic to the implementations of the underlying networks and support various future networking technologies. The architecture should also support the flexible deployment of ML functionalities in the underlying network including the distributed placement and composition of ML functionalities. Another important aspect of the requirement for the architecture is to enable flexible management of ML functionalities and the integration of the management and orchestration for both ML functions and network functions [37]. The high-level structure of the ITU-T architecture for integrating ML in future networks is depicted in Figure 4. This architecture comprises three subsystems--the ML pipeline subsystem, the management subsystem, and the ML sandbox subsystem [38]. An ML pipeline is a set of logical nodes, each with specific functionalities, that can be combined to form an ML application in a network. An ML pipeline comprises the following types of nodes. The Source (SRC) node is the source of data that can be used as the input to the ML pipeline. Examples of source nodes include user devices and some NFs defined in the 5G SBA such as SMF and AMF. The Collector (C) node collects data from one or more SRC nodes. The Pre-Processor (PP) node is responsible for cleaning, aggregating, and preparing the data to be in a suitable format for the ML model. The Model (M) node is an ML model used in the pipeline, for example a classification or prediction function. The Policy (P) node enables the application of policies to the output of the model node. The Distributor (D) node is responsible for identifying the sink node(s) and distributing the output of the M node to the corresponding sink node(s). The Sink (SINK) node is the target of the ML output on which it takes actions. The workflow of information process in an ML application can be represented by the chaining of nodes in an ML pipeline. The data from various source nodes in the underlying networks are collected (by the C node) and pre-processed (by the PP node) before the data are analyzed by the ML model (at the M node). The output of the ML model is then used to apply policies (by a P node) that will be implemented (by a SINK node). An ML application can be realized by instantiating the ML pipeline nodes as VNFs and associating them with the technology-specific underlying network functions, based on the corresponding requirements of the ML application and the capabilities of the underlying network functions. The management subsystem extends the management and orchestration capabilities for network functions and services to ML pipeline nodes, thus enabling uniformity to the management of ML functionalities and network/compute functions in future networks. This subsystem includes NFV MANO functions such as VIM, NFVM, and VNFO and defines the ML Function Orchestrator (MLFO) as a core component. The MLFO is responsible for managing and orchestrating ML pipeline nodes based on the ML intent and/or dynamic network conditions. The MLFO selects the ML model for each ML pipeline based on factors such as the model performance, manages placement of ML pipeline nodes based on the corresponding network capabilities and ML application constraints, and provides chaining functions to connect ML nodes together to form an ML pipeline. As shown in Figure 4, ML intent also plays an important role in the management subsystem as the input to the MLFO.