quality_assurance · workflow
LLMOps in restricted BYO networks using Azure Machine Learning with conditional continuous evaluation
The team needed to establish an LLMOps and continuous evaluation pipeline in a restricted bring-your-own network, facing service configuration complexity, SDK limitations in private networks, and a 6-hour end-to-end evaluation run that blocked deployments on every commit.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · PR creation triggers CI
A pull request from a feature branch to the develop branch triggers CI checks.
Tools used
Prompt FlowAzure Machine Learningpfazure SDKAzure Container RegistryAzure Web AppDockerGitHub
Outcome
The team implemented a CI/CE/CD pipeline with an opt-out mechanism for the long-running E2E evaluation, allowing the ~30-minute minimal dataset run to proceed before every dev deployment while the full ~6-hour evaluation runs in parallel.
What failed first
The AML managed endpoint did not work with the private network and required a fallback to Docker packaging; the pfazure SDK had compute host resolution issues; and obtaining Service Principal permissions was a lengthy process.
Results
Time saved~6hrs
Grounding & classification
Source type: technical build writeup
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conversational aifailure mode describedhuman review describedmetric backedproduction runtime claimedtools describedworkflow describedcycle time reductiontechnical build writeupquality assurance