  ML Data Curation process typically consist of heterogeneous & federated
source systems with varied schema structures; requiring curation process to
standardize metadata from different schemas to an inter-operable schema. This
manual process of Metadata Harmonization & cataloging slows efficiency of
ML-Ops lifecycle. We demonstrate automation of this step with the help of
entity resolution methods & also by using Cogntive Database's Db2Vec embedding
approach to capture hidden inter-column & intra-column relationships which
detect similarity of metadata and then predict metadata columns from source
schemas to any standardized schemas. Apart from matching schemas, we
demonstrate that it can also infer the correct ontological structure of the
target data model.
