This project walks through the homework for Module 3 in DataTalk's MLOps Zoomcamp. The project can be started by running: 'docker compose up', which starts Mage on port 6789 and MLflow on port 5000. Mage is used to build a pipeline that ingests and transforms NYC taxi data, trains a linear regression model with sklearn, logs the model and the artifact (dict vectorizer), and then registers the model with MLFlow.
Start Mage: docker compose up'
Version Number: v0.9.70
Shut Down: docker compose down
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
Answer: 55
Let's create an ingestion code block.
In this block, we will read the March 2023 Yellow taxi trips data.
How many records did we load?
3,003,766 3,203,766 3,403,766 3,603,766
Answer: 3,403,766
Added a Transformer block for Data Preperation.
What's the size of the result?
2,903,766 3,103,766 3,316,216 3,503,766
Answer: 3,316,216
We will now train a linear regression model using the same code as in homework 1
Fit a dict vectorizer Train a linear regression with default parameres Use pick up and drop off locations separately, don't create a combination feature Let's now use it in the pipeline. We will need to create another transformation block, and return both the dict vectorizer and the model
What's the intercept of the model?
Hint: print the intercept_ field in the code block
21.77 24.77 27.77 31.77
Answer: 24.77
Next, start the compose again and create a data exporter block.
In the block, we
Log the model (linear regression) Save and log the artifact (dict vectorizer) If you used the suggested docker-compose snippet, mlflow should be accessible at http://mlflow:5000.
Find the logged model, and find MLModel file. What's the size of the model? (model_size_bytes field):
14,534 9,534 4,534 1,534
Answer: 4,534
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