 Hello. Now that you've seen how to link a model to the model registry with code, I want to go over the UI a bit and help orient you to the model registry and the various components within it in the UI, because it's actually very useful. So to see the model registry from your homepage, in the left-hand side, you'll see a list of your projects, you'll see applications, and you'll see this model registry. And one way you can see all of your model, your registered model. So this model registry is a specific product in Weights and Biases, and the model registry has a list of registered models. And again, the registered models are just tasks. These are different tasks that represent, you know, different use cases that you might have, that you're trying to tackle with, you know, specific models. And if you recall in the last video, we created this MyML task. Now I want to show you a little bit more of a professional example. And so I'm going to go to this registered model here. And this is this fine tune, this fine tuning LLM model that we're working on. And what you see here is all the different versions of this model you can see the different aliases see what is in what's the latest a model which one is in production which one's at staging so on and so forth we can click view details and view details this is where you can see the model card and the model card is really helpful, especially like if you're working on teams and there's lots of different models, there's lots of different people collaborating. And you want a look like a central place where you have information about the registered model, about the task really that you're trying to tackle. And so, you know, things like, you know, as much detail as you think is helpful, but it's helpful to actually standardize this across your team, across your company, so that when people see a model card, they know, they know what they're looking at. They have like a kind of lay of the land. They know, okay, like where to find information and like different people do this differently if you want like a good template or good like mental model this is like somewhat similar to the idea on of hugging face model cards is like hey give you like all the information that you might think are relevant about a model so in this case what we have here is model details, you have like you know what the training data is, how long it took, the hardware, the intended use case, some performance metrics, you know limitations, ethical considerations, things like that. Now we have a more detailed view of aversions. Now we can get into things like who logged these models. We can get into some of the more important metrics about the models. And, you know, this is a very helpful view to kind of quickly scan all the versions. And then below this, we'll have automations. So this lists the different types of automations that we have. In this case, we have webhooks and jobs, which we'll get into in a separate section. This actually helps us to glue sort of other systems to weights and biases and use the modeled registry as a central hub to manage the lifecycle of models. central hub to manage the lifecycle of models. Next thing we can do is we can click on one of the versions. I'm going to go ahead and click on this latest one. This is where we can see different tabs. This version tab, you've seen it before, gives you a high-level overview. Metadata is useful. Metadata, it promotes this information that has been logged at the run level and it promotes it upwards all the way to this level so that you don't have to go dig for it. It'll tell you all the summary metrics and other parameters that you might have logged it's really useful just to be able to see it quickly on this high level you can you can also search for things which is very useful uh this is one of my favorite tabs i use this all the time because i don't like to memorize code is that you could just copy and paste okay how to download this artifact which is really useful you can see the files these are different files um that belong to this specific artifact or this model and like these are all the like parts that you might need to load this model for example like commonly you might need a config, you know, plus model weights and maybe some other things. So actually this is like, you know, the assets that you need for that model. And I think like one of the most popular tabs is this lineage tab. This lineage tab actually shows you what processes created this artifact. Noah, do you want to talk a little bit about this lineage tab and what's going on here? Yeah. I think we wanted to spend a little bit more time on this tab specifically because it's one of the most useful one in our opinion, but I think to a lot of the customers we talk to. And it's, as we see up here, it's our lineage tab. If you remember when I spent some time earlier talking about why the model registry is so critical for teams who are kind of enterprise teams that are doing model management, lineage was one of the key features that I mentioned being critical to things like governance. So how, as an ML team, can you communicate to security stakeholders, compliance, auditors, that you know the exact history of essentially construct this graph that has as nodes runs and the different artifacts that are used as input and output. So you can see this is a really great example. It's pretty simple and output. So you can see this is a really great example. It's pretty simple and straightforward. We have a training run that's happening, and it's taking in this data set as input and might also be using some kind of automated job as well. And as output, there's a model. So if you remember back to earlier when I mentioned, you know, we're working with customers that have to report this information back. And some of the big questions tend to be around what I have this model in production, what is the data set that was used? So this is a very simplified example of how you can kind of trace back, you know, you have a model, what was the exact version of the data set? And you'll note note here that the dataset box actually represents a specific version. That way, it's not just largely what data was used, but it's the specific snapshot of data that went into train this model. That's great. I really like this lineage view because these are not just like, you know, this is not just like some kind of artwork that you can click on this and you can take you to run, which is super cool. You don't have to hunt around and figure out like, you know, it's actually really nice to be able to look at that. Noah, what's going on here with like these different types of styles and like what's when might you want to think about this slider thing? Yeah, great question. So the style is a way for you to configure kind of how much information you want summarized versus, you know, if you click the complete graph that might show it's a zoom out and you can see all of the essentially like peer runs so that's hidden in the detailed the detailed view but complete for example might be the the largest level you can zoom out and see really every single node that's happening in parallel and then simple kind of goes in the reverse direction. You might just see a summary of information and this kind of groups a bunch of the artifacts together. So depending on what questions you're trying to answer, you know, you can play around with the different configurations there. And typically I find that direct lineage is the most common. And folks will kind of like walk up and down that graph. And then one thing I wanted to mention as well as, you know, we, this, this graph can all be navigated programmatically as well. So if in your code, you're trying to find kind of the, the artifact that was consumed for, you know, by, by the run that you're in, then it's also possible to move up the graph. So kind of check out the ancestors of the artifact and then down as well, all of its children nodes. Generated artifacts. So you'll notice that there's some system level artifacts that we'll use to store information. So one example is, you know, like run tables. So when you're logging a table to weights and biases, we actually store that under the hood as an artifact. Some other examples might be a launch job. So those are kind of the configurableable reusable job templates that we'll spend some time getting into later. But that's another example of an auto-generated artifact. So these are system level artifacts that we use to store kind of other stuff that you're logging in weights and biases. And so it might be helpful to see those in the lineage graph, and it might also be kind of cluttering it so that way we give the option there. Great, thank you that's really helpful. And yeah that's that's it that's overview of the user interface for a model registry and how to navigate the most important features.
Navigating Model Registry
Now that we know how to link your model, lets dive into the Model Registry UI. 

If you would like to click around and check out how the model registry looks in use, look here: https://wandb.ai/reviewco/registry/model?utm_source=courses&utm_medium=courses&utm_campaign=emm