How to think about orchestration for ML pipelines on GCP
https://datatonic.com/insights/kubeflow-pipelines-cloud-composer-data-orchestration/
Best practices talk about kubeflow pipelines
https://www.youtube.com/watch?v=TsALZXmdKSg
Some big mlops landscape overview
Also if you have more time for this topic, highly recommend going through this 40-pages long whitepaper “Practitioners guide to MLOps” https://services.google.com/fh/files/misc/practitioners_guide_to_mlops_whitepaper.pdf
Model and data versioning with Model Registry
MLOps process lvl1 vs lvl2
https://cloud.google.com/architecture/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning
Thorough course on metrics for ML Observability (not exactly needed for the exam, just very good relevant thing)