mated evaluation using synthetic generated queriesFor a broader evaluation we can examine a larger number of queries to check the retrieval component's performance. We do this by using an LLM to generate synthetic data. In our case we take the text of each document chunk and pass it to an LLM, telling it to generate a question.

For example, given the text:

zenml orchestrator connect ${ORCHESTRATOR\_NAME} -iHead on over to our docs to

learn more about orchestrators and how to configure them. Container Registry export

CONTAINER\_REGISTRY\_NAME=gcp\_container\_registry zenml container-registry register $

{CONTAINER\_REGISTRY\_NAME} --flavor=gcp --uri=<GCR-URI> # Connect the GCS

orchestrator to the target gcp project via a GCP Service Connector zenml

container-registry connect ${CONTAINER\_REGISTRY\_NAME} -i Head on over to our docs to

learn more about container registries and how to configure them. 7) Create Stack

export STACK\_NAME=gcp\_stack zenml stack register ${STACK\_NAME} -o $

{ORCHESTRATOR\_NAME} \\ a ${ARTIFACT\_STORE\_NAME} -c ${CONTAINER\_REGISTRY\_NAME}

--set In case you want to also add any other stack components to this stack, feel free

to do so. And you're already done! Just like that, you now have a fully working GCP

stack ready to go. Feel free to take it for a spin by running a pipeline on it.

Cleanup If you do not want to use any of the created resources in the future, simply

delete the project you created. gcloud project delete <PROJECT\_ID\_OR\_NUMBER> <!--

For scarf --> <figure><img alt="ZenML Scarf"

referrerpolicy="no-referrer-when-downgrade" src="https://static.scarf.sh/a.png?

x-pxid=f0b4f458-0a54-4fcd-aa95-d5ee424815bc" /></figure> PreviousScale compute to the

cloud NextConfiguring ZenML Last updated 2 days ago

we might get the question:

How do I create and configure a GCP stack in ZenML using an

orchestrator, container registry, and stack components, and how

do I delete the resources when they are no longer needed?