From the course: Full-Stack Deep Learning with Python
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Setting up MLflow on the local machine - Python Tutorial
From the course: Full-Stack Deep Learning with Python
Setting up MLflow on the local machine
- [Instructor] While working on Colab, we don't have access to the underlying virtual machine that hosts our cloud notebook, which is why we had to use Ngrok to access MLflow running on Colab. Now, if you want to be able to serve and deploy our models using MLflow, well Colab is just not going to work. So I'm going to use a little work around and set up MLflow on my local machine and we'll serve the model that we trained on Colab on our local machine. This will involve downloading the artifacts trained on Colab, setting up the same directory structure on our local machine, and then using MLflow to deploy and serve the model artifacts. First thing here on my local machine, let's make sure I have Python installed. Python --version tells me I have Python 3.10.9, the same version of Python that we use to train our model. We'll be using a locally running Jupyter Notebook, so I'm going to check whether I have Jupyter…
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