Development, Operation and Data Governance for ML-based Software Systems (DOGO4ML). DOGO4ML proposes a holistic end-to-end framework to develop, operate and govern MAchine Leaning Software Systems (MLSS) and their data. This framework revolves
around a new proposal we call the DevDataOps lifecycle, which unifies two software lifecycles: a DevOps lifecycle and a DataOps
lifecycle. The DevOps cycle aims to transform the requirements of an MLSS into deployed code (Dev) and get feedback as soon as
possible from the end-users (Ops) that can be used to evolve the requirements (including those that apply to the ML models). The
DataOps cycle provides support to the data management and analysis processes that characterise MLSS. The DataOps processes are
inter-related with those in the Dev phase of the DevOps software cycle, since they produce the required ML models (created through
several iterations in the DataOps lifecycle) to be embedded into the ML software components of the MLSS. Further, the DataOps lifecycle
aims to get feedback from the data analysts to continuously improve the data management and analysis processes.