 Hey Noah, thanks a lot for helping me kind of do this overview of the model registry and sort of the nuts and bolts around it. You know, in the early days of Weights and Biases, I built a lot of what I would call enterprise tools myself, gluing things together. And you know, I know that you have created a lot of features now that make it really easy for people so they don't have to make their own tools. They can just have one or two lines of code and glue things together. Can you talk a little bit about some of those enterprise features or some of the notable ones that come to your mind and what what's like really popular and what's on what's on your mind yeah sure um wow where where to begin uh there's a whole host of of good stuff but i think um one of the one one of the i think most important ones to bring up that folks who are already using um artifacts might already be familiar with and it's less specific to the model registry. But, you know, we're talking about enterprise teams that typically might work, they might have all of their models and data sets stored somewhere else. And if you've been following this course and seeing, you know, the demos on model registry and you're like, that's great, but we're already storing all of our model files in S3. How is this relevant to me? I don't really want to download them, move them over to kind of locally upload them to Weights and Biases. So I'm going to pull up kind of this documentation here which talks through how you can actually track files that live not necessarily in weights and biases but externally. So you know the most common examples we see customers working with are they have their models maybe saved in S3 or any of the other major cloud providers like Azure or in GCS And you can actually just similar like how you would add a file to the artifact. If we take a look at this code snippet here, you can have your artifacts store a reference to any location inside a bucket. So that way you're not kind of dealing with moving things over to weights and biases and you're leaving everything inside the buckets as is but you're kind of adding pointers to where where those live and it's instead of doing add file or add directory then you would directly just being be adding a reference here so depending on you know which type of provider you're working with and we also work with nfs on which type of provider you're working with. And we also work with NFS drives as well. We have some great guides which specifically walks through this, but I would take a look at the documentation and that's more of a high level. Hey, you don't need to be moving stuff out of your buckets. The advantages of still using weights and biases to kind of mark your models and linking them to the registry is almost to be this layer on top of, let's say, S3, where you're able to get all of the features around automatic versioning and lineage. So that kind of graph view we talked about by using the the APIs where the the storage the underlying storage is still going to be kind of your cloud provider. Cool and then the other thing I wanted you know yeah on the the story of important features to call out for enterprise customers I also wanted to talk through protected aliases. So we spent a good amount of time talking about aliases and how they're relevant in the model registry to track the life cycle of kind of which version is in which state of the model life cycle. And so this concept of protected aliases is that you can mark a few aliases as being kind of admin only, as in not everyone will be able to add the alias production or evaluation. And typically we see customers using kind of a key set of, you know, the important aliases that mark like production, that not everyone should be able to promote a model to this state. And why is this important? Because sometimes you might have an alias like production that is actually going to kick off this automation. As we talked about, you can use an alias to kick off an automation. And so you want to make sure that not everyone in the team is, you know, adding this production alias. So that's another feature to call out to help users kind of control or help team admins control, you know, save a special set of aliases to only be added by folks that are under this role of a model registry admin, which you can see above. And that's managed inside, the docs covers this, but you can find it inside kind of the settings of your model registry.
Enterprise Model Management features
Tune in to Hamel and Noa sharing some tips on how to take advantage of enterprise-grade model management: 

-Using external files docs: https://docs.wandb.ai/guides/artifacts/track-external-files?utm_source=courses&utm_medium=courses&utm_campaign=emm#docusaurus_skipToContent_fallback

-Protected aliases docs: https://docs.wandb.ai/guides/model_registry/access_controls?utm_source=courses&utm_medium=courses&utm_campaign=emm#add-protected-aliases