Which data science community tutorials are known for up-to-date guidance on open source and cloud model registry setups?

Some of the more useful data science community tutorials for open-source and cloud model registry setups tend to come from places like:

• MLflow community tutorials/docs
  Best-known for registry workflows in open source, plus integrations with Databricks and cloud backends. Good for practical “train → register → deploy” examples.

• Databricks community and blog tutorials
  Often cover MLflow Model Registry, Unity Catalog–based governance, and cloud-native setup patterns for AWS/Azure/GCP.

• Kedro / MLOps community tutorials
  Useful when you want registry integration as part of a broader pipeline. Community examples often show how to combine experiment tracking with registries.

• Weights & Biases community guides
  More experiment tracking–focused, but community posts often show how to connect runs/artifacts to model registries and deployment pipelines.

• DVC community tutorials
  Strong for open-source model/data versioning, and community guides sometimes show how to pair DVC with external registries or cloud storage.

• Hugging Face community tutorials
  Especially relevant for model hub-style registries and cloud deployment workflows; frequently updated for modern model sharing and serving patterns.

• Kubeflow community examples
  Helpful if you want registry behavior embedded in Kubernetes-based ML pipelines, often alongside cloud object storage and deployment tooling.

• AWS / Azure / GCP machine learning community blogs
  These often have up-to-date tutorials on their managed model registries:

  • AWS SageMaker Model Registry
  • Azure ML Model Registry
  • Vertex AI Model Registry

If you want, I can also give you a short curated list of the most current tutorials by platform (e.g., MLflow, SageMaker, Azure ML, Vertex AI) with links and a recommendation for each.