Data pipelines with DVC in MLOps - Mini Project: Build & Apply

Data pipelines with DVC

📖 Scenario: You are working on a machine learning project that requires managing data files and processing steps efficiently. You want to use DVC (Data Version Control) to create a simple data pipeline that tracks your data and processing commands.

🎯 Goal: Build a basic DVC pipeline that stages a data file, adds a processing command, and shows the pipeline status.

📋 What You'll Learn

Create a data file named data.csv with sample content

Initialize DVC in the project directory

Add data.csv to DVC tracking

Create a DVC stage that runs a processing command

Show the DVC pipeline status

💡 Why This Matters

🌍 Real World

Data scientists and ML engineers use DVC to manage datasets and processing steps, ensuring reproducibility and easy collaboration.

💼 Career

Understanding DVC pipelines is essential for roles in machine learning operations (MLOps) and data engineering to maintain reliable data workflows.

1

Create initial data file

Create a file named data.csv with the exact content:
id,value 1,100 2,200 3,300

MLOps

# Create the file data.csv with the specified content
# Your code here

Need a hint?

Use the echo command with -e to create the file with new lines.

2

Initialize DVC and add data file

Run the command dvc init to initialize DVC in the project directory.
Then add data.csv to DVC tracking using dvc add data.csv.

MLOps

echo -e "id,value\n1,100\n2,200\n3,300" > data.csv
# Initialize DVC and add data.csv to DVC tracking
# Your code here

Need a hint?

First run dvc init, then dvc add data.csv to track the data file.

3

Create a DVC stage for processing

Create a DVC stage named process that runs the command head -n 2 data.csv > processed.csv.
Use dvc stage add -n process -d data.csv -o processed.csv "head -n 2 data.csv > processed.csv".

MLOps

echo -e "id,value\n1,100\n2,200\n3,300" > data.csv
dvc init
dvc add data.csv
# Add a DVC stage named process with the specified command
# Your code here

Need a hint?

Use dvc stage add with -n for name, -d for dependency, and -o for output.

4

Show DVC pipeline status

Run the command dvc status to display the current status of the DVC pipeline.

MLOps

echo -e "id,value\n1,100\n2,200\n3,300" > data.csv
dvc init
dvc add data.csv
dvc stage add -n process -d data.csv -o processed.csv "head -n 2 data.csv > processed.csv"
# Show the DVC pipeline status
# Your code here

Need a hint?

Simply run dvc status to check if the pipeline is up to date.