Edge deployment of cloud-originated machine learning and artificial intelligence workloads

A method comprising:

• transmitting, from a physically containerized edge data center, a request corresponding to a pre-trained machine learning model, wherein the physically containerized edge data center includes a fleet of edge compute units;
• receiving, from a cloud management platform and by the physically containerized edge data center, the pre-trained machine learning model, wherein the physically containerized edge data center is deployed to an edge location and configured to obtain, via a network, one or more data streams of a plurality of edge assets at the edge location, wherein the one or more data streams include at least environmental data related to the edge location;
• transmitting, from the physically containerized edge data center, one or more batch uploads of information associated with inference performed by the physically containerized edge data center using the pre-trained machine learning model and the one or more data streams, wherein the one or more batch uploads of information are transmitted to the cloud management platform;
• and
• receiving, by the physically containerized edge data center, one or more updated machine learning models generated by the cloud management platform responsive to the one or more batch uploads of information, wherein the one or more updated machine learning models are based on retraining or finetuning of the pre-trained machine learning model with the one or more batch uploads of information,
• wherein the physically containerized edge data center is configured with a hardware specification associated with an edge site where the physically containerized edge data center is deployed.