Digital Twin (DT) has promising impact on the life cycle management of assets in manufacturing industry. The concept of DT has become possible with digitalisation and Artificial Intelligence (AI). Data driven Machine Learning (ML) capabilities, can enhance the performance of the DT. To replicate a dynamic system, the DT should continuously receive and process incoming data in real-time. However, every time that the system receives new incoming datasets, the challenges of ML such as data preparation, feature selection, model selection and performance evaluation, slow down the development process of DT. This paper proposes a MetaAnalyser platform that automates these steps for incoming datasets in real-time. The MetaAnalyser platform through automating data preparation, feature selection, model selection and performance evaluation, is expected to increase the level of agility in the development process of DT and the efficiency of the DT during its lifecycle. The MetaAnalyser platform is demonstrated in this paper by ranking the features that affect the arrival delays in trains and ranking regression models based on their performance on the dataset.