From the course: Learn Databricks GenAI
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Evaluate fine-tuning MLflow experiments - Databricks Tutorial
From the course: Learn Databricks GenAI
Evaluate fine-tuning MLflow experiments
- [Instructor] Now as we're moving to the UI to look at the created artifacts, let's first start with the endpoint. So if we go over to the endpoint, you can see that our new model is served up on this endpoint, and here is our new model. And if we click into it, you can see that our model is available in the catalog and it has the prefix classif, because this is a custom trained model that is different than the foundational model. It has our additional data in it, so you can see it's versioned. And then we have, you know, details and permissions. To me, the most interesting part about this though is the experiment. So we can, using the web UI run experiments, we use the notebook just because it was more concise, but this is available in the web UI as well. So we have chat completion. The other two types, we select our foundational model. The smaller the better, of course, the training data, which has to be in the correct format as we saw. We register to a location, so we select a…
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