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Describe MLflow model workflow in Databricks

Describe MLflow model workflow in Databricks - Azure Tutorial

From the course: Microsoft Azure Data Scientist Associate (DP-100) Cert Prep

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Describe MLflow model workflow in Databricks

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- [Instructor] Let's take a look at Mlflow, an open source model tracking and model versioning system that comes with Databricks, but also is able to be used by itself. It has a lot of exciting features. And to start off with, let's just walk through the quickstart guide here before I go in and show you how to use it. First step, we have pip install mlflow. This is how you get started. And then if we scroll down here, you can see that when you want to go to the tracking API here, as long as you've gone through and done a git clone of the MLflow repo, you can CD into the examples subdirectory and then actually go through all of these examples. And in this case, for example, the MLflow tracking API, you could log metrics and artifacts. So for example, if you wanted to go in and measure the last time it trained and maybe some hyper parameters you used, this is where you would actually go through here and put that. If you want to take a look at the user interface to let's say look at…

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