From the course: Applied Machine Learning: Value Estimation
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MLflow overview
From the course: Applied Machine Learning: Value Estimation
MLflow overview
- [Instructor] In this section, we're going to talk about deployment. The first thing that we need to do if we're going to deploy this model is we need to have a way to persist it. So we're going to use the joblib library, and joblib will allow us to serialize our model. I'm going to stick this in a directory called app. So I'm going to use the capability of Jupyter to call a shell command. In Jupyter, if you have a code cell that starts with an exclamation point, it's not actually running Python code, it's going to be running code on the computer. So this is just going to say, "Make a directory called app," and we can just come over here and validate that we have an app directory. It looks like we do have an app directory now. I'm going to serialize our model into that. Now, this probably isn't going to work, because this is using the original tuned model. And since I created this, my code space stops, so I'll just run it. I'm pretty confident it won't work. Yeah, xg_tuned is not…
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