Your organization is building a CI/CD pipeline for a set of microservices that expose different trained machine learning models. One service still depends on TensorFlow 1.15 while another requires TensorFlow 2.14. During test deployments the pipeline installs all Python packages to the host OS and one model frequently crashes because a newer framework version overwrote the older one. To apply the DevOps/MLOps principle of code isolation, which action best prevents these dependency collisions and guarantees the same runtime across development, test, and production environments?
Keep separate Git branches for each service (dev, staging, prod) so conflicting libraries never coexist in the same repository.
Mount a shared requirements directory on every host so all services install packages from the same location at startup.
Store pre-compiled wheels for every framework in an artifact repository and let production servers install them into the system Python.
Build and deploy each model as its own Docker container image that pins framework versions; run the same image in every environment.
Code isolation in DevOps and MLOps means packaging an application together with the exact libraries, configuration, and runtime it needs, so that nothing outside the package can change its behavior. Building each model as a version-controlled Docker (or OCI-compatible) container image gives every service its own fully self-contained environment. The image is built once by the pipeline and then run unchanged in every stage, eliminating the "works on my machine" problem, avoiding framework overwrites, and making rollbacks straightforward. The other options still share a system-wide Python installation, rely on runtime toggles, or only separate code in Git; none of those approaches truly isolate the executable environment or guarantee reproducibility.
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