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4: Review the Experiments & Select the Best Model

Objective

In this lab, we will:

  • Review the performance of different models and runs
  • Select the Model with the best performance

Guide

Step 1 - Find and Open the Jupyter Notebook

Please open the notebook, "04_model_registration.ipynb" in the same directory "workshop_materials/bike_demand_forecasting".

Step 2 - Set the MLflow Remote Tracking Server

💡 Note: The link to the MLflow server will be provided during the workshop! You should replace the MLFLOW_REMOTE_TRACKING_SERVER with this provided URL.

Step 3 - Set a Dummy Name or your Firstname (It should be unique!)

💡 Note: There is only one instance of MLflow Server for all the participants. So in order to avoid any confusion, please make sure that you put an unique name!

You should replace the YOUR_FIRSTNAME with a dummy name or your firstname.

Step 4 - Review the Experiments' Results

At this step, you should just run the cell and review the output. Analyzing the results (i.e. comparing the metrics), you'll identify the best-performing model configuration and select it for further evaluation or deployment.

Step 5 - Select the Best-Performing experiment

When we prompted in the notebook, please select the run (run-ID) which has performed the best.

✅ Now with a model to deploy, we see how to prepare the deployment files and dependencies in a container, in the next exercise Model Deployment - Containerize the Endpoint-API.