A method for orchestrating acquisition of a quantity of communication network data for training a target Machine Learning, ML, model for use by a communication network node, the method, performed by an orchestration node, comprising: obtaining a representation of a data acquisition state for the communication network data; using an orchestration ML model to map the representation of the data acquisition state to a first amount of the communication network data to be collected from sources of the communication network data, and a remaining amount of the communication network data to be generated using a generative model; causing at least the first amount of the communication network data to be collected from sources of the communication network data; when the collected communication network data fulfils an availability criterion for training of a generative model, causing a generative model for the communication network data to be trained using the collected communication network data; causing the remaining amount of the communication network data to be generated using the trained generative model; and causing the quantity of communication network data, comprising the first amount of collected data and the remaining amount of generated data, to be provided to the communication network node for training of the target ML model; wherein a representation of a data acquisition state for the communication network data comprises: a number of target ML models to be trained using the communication network data; a cost of resources for collection of the communication network data from data sources; and a cost of resources for generating the communication network data. obtaining a representation of a data acquisition state for the communication network data; using an orchestration ML model to map the representation of the data acquisition state to a first amount of the communication network data to be collected from sources of the communication network data, and a remaining amount of the communication network data to be generated using a generative model; causing at least the first amount of the communication network data to be collected from sources of the communication network data; when the collected communication network data fulfils an availability criterion for training of a generative model, causing a generative model for the communication network data to be trained using the collected communication network data; causing the remaining amount of the communication network data to be generated using the trained generative model; and causing the quantity of communication network data, comprising the first amount of collected data and the remaining amount of generated data, to be provided to the communication network node for training of the target ML model; wherein a representation of a data acquisition state for the communication network data comprises: a number of target ML models to be trained using the communication network data; a cost of resources for collection of the communication network data from data sources; and a cost of resources for generating the communication network data. a number of target ML models to be trained using the communication network data; a cost of resources for collection of the communication network data from data sources; and a cost of resources for generating the communication network data.