Module 3: Orchestration Source mlops-zoomcamp/03-orchestration at main · DataTalksClub/mlops-zoomcamp (github.com) Homework The goal of this homework is to train a simple model for predicting the duration of a ride, but use Mage for it. We'll use the same NYC taxi dataset , the Yellow taxi data for 2023. Question 1. Run Mage First, let's run Mage with Docker Compose. Follow the quick start guideline. What's the version of Mage we run? (You can see it in the UI) Answer of Question 1: v0.9.71 Question 2. Creating a project Now let's create a new project. We can call it "homework_03", for example. How many lines are in the created metadata.yaml file? 35 45 55 65 Solution docker exec -it mlops-magic-platform-1 bash root@4c0edc9c9a86:/home/src# cd mlops root@4c0edc9c9a86:/home/src/mlops# mage init homework_03 root@4c0edc9c9a86:/home/src/mlops# cd homework_03 root@4c0edc9c9a86
Module 2 – Experiment-Tracking Source https://github.com/DataTalksClub/mlops-zoomcamp/tree/main/02-experiment-tracking Homework Q1. Install MLflow To get started with MLflow you’ll need to install the MLflow Python package. For this we recommend creating a separate Python environment, for example, you can use conda environments, and then install the package there with pip or conda. Once you installed the package, run the command mlflow –version and check the output. What’s the version that you have? import mlflow mlflow . __version__ '2.13.0' Answer of Q1: 2.13.0 Q2. Download and preprocess the data We’ll use the Green Taxi Trip Records dataset to predict the duration of each trip. Download the data for January, February and March 2023 in parquet format from here. Use the script preprocess_data.py located in the folder homework to preprocess the data. The script will: load the data from the folder <TAXI_DATA_FOLDER> (the folder where you have downloaded the data), fit a