Industrial MLOps as a Distributed Operating Model
Industrial MLOps is the discipline of managing ML systems across distributed environments with different trust boundaries, operational constraints, and availability requirements.
When people talk about MLOps, they often imagine a fairly straightforward lifecycle.
A team trains a model, validates it, packages it, and deploys it to production. Even if the infrastructure is split into development, staging, and production environments, those environments usually belong to the same IT landscape, have stable connectivity, and are managed according to relatively consistent principles.
In an industrial company, that assumption often stops being true.
A single ML system may span multiple geographic sites, cross corporate and technological networks, interact with industrial equipment, operate inside partially isolated segments, and still be expected to remain functional when connectivity to the central environment is temporarily unavailable.
That is why industrial MLOps is not simply “more complex CI/CD for models.”
It is about managing ML systems across distributed infrastructure...
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