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

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

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

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

Hint

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

Practice

1. What is the main purpose of using dvc repro in a DVC pipeline?

easy

A. To delete all pipeline data and cache

B. To initialize a new DVC repository

C. To reproduce pipeline stages and update outputs if inputs changed

D. To manually edit pipeline stage commands

Solution

1. Step 1: Understand the role of dvc repro

   This command checks if any inputs or dependencies of pipeline stages have changed.

2. Step 2: Effect of running dvc repro

   If changes are detected, it reruns the affected stages to update outputs accordingly.

3. Final Answer:

   To reproduce pipeline stages and update outputs if inputs changed -> Option C

4. Quick Check:

   dvc repro updates pipeline outputs [OK]

Hint: Remember: repro means rerun changed pipeline parts [OK]

Common Mistakes:

• Confusing repro with initialization commands
• Thinking repro deletes data
• Assuming repro edits pipeline commands

2. Which of the following is the correct syntax to add a pipeline stage with DVC that runs python train.py and outputs model.pkl?

easy

A. dvc stage add -n train -o model.pkl python train.py

B. dvc add stage train -o model.pkl python train.py

C. dvc run -n train -o model.pkl python train.py

D. dvc stage add -n train -d train.py -o model.pkl python train.py

Solution

1. Step 1: Identify required flags for stage creation

   The dvc stage add command requires -n for name, -d for dependencies, and -o for outputs.

2. Step 2: Check which option includes all required flags correctly

   dvc stage add -n train -d train.py -o model.pkl python train.py uses -n train, -d train.py (dependency), and -o model.pkl with the command python train.py.

3. Final Answer:

   dvc stage add -n train -d train.py -o model.pkl python train.py -> Option D

4. Quick Check:

   Stage add needs name, dependency, output flags [OK]

Hint: Stage add needs -n (name), -d (deps), -o (outputs) [OK]

Common Mistakes:

• Omitting the dependency with -d
• Using deprecated dvc run instead of stage add
• Mixing order of flags incorrectly

3. Given this DVC pipeline stage definition in dvc.yaml:

stages:
  preprocess:
    cmd: python preprocess.py data/raw data/processed
    deps:
      - data/raw
      - preprocess.py
    outs:
      - data/processed

What happens when you run dvc repro after modifying data/raw?

medium

A. The preprocess stage reruns and updates data/processed

B. Nothing happens because only preprocess.py changes trigger rerun

C. The pipeline fails due to missing output specification

D. All pipeline stages rerun regardless of changes

Solution

1. Step 1: Identify dependencies of the preprocess stage

   The stage depends on data/raw and preprocess.py.

2. Step 2: Effect of changing data/raw on dvc repro

   Changing a dependency triggers rerun of that stage to update outputs.

3. Final Answer:

   The preprocess stage reruns and updates data/processed -> Option A

4. Quick Check:

   Changed input triggers stage rerun [OK]

Hint: Change in deps triggers rerun of that stage [OK]

Common Mistakes:

• Assuming no rerun if only data changes
• Thinking all stages rerun always
• Confusing outputs with dependencies

4. You run dvc repro but get an error: ERROR: failed to reproduce stage 'train': missing dependency 'data/train.csv'. What is the most likely cause?

medium

A. The file data/train.csv was deleted or moved after pipeline creation

B. The dvc.yaml file is missing the train stage

C. The dvc.lock file is corrupted

D. You forgot to run dvc init before dvc repro

Solution

1. Step 1: Understand the error message

   The error says a dependency file is missing, which means DVC cannot find data/train.csv.

2. Step 2: Common causes of missing dependency errors

   Usually, the file was deleted, renamed, or moved after the pipeline stage was created.

3. Final Answer:

   The file data/train.csv was deleted or moved after pipeline creation -> Option A

4. Quick Check:

   Missing dependency file causes repro error [OK]

Hint: Check if all dependency files exist before repro [OK]

Common Mistakes:

• Assuming dvc.yaml missing stage causes this error
• Blaming dvc.lock corruption without evidence
• Forgetting to initialize repo before repro

5. You want to create a DVC pipeline with two stages: extract that outputs data/raw.csv, and train that depends on data/raw.csv and outputs model.pkl. Which sequence of commands correctly sets up this pipeline?

hard

A. dvc stage add -n train -o model.pkl python train.py

dvc stage add -n extract -d data/raw.csv -o data/raw.csv python extract.py

B. dvc stage add -n extract -o data/raw.csv python extract.py

dvc stage add -n train -d data/raw.csv -o model.pkl python train.py

C. dvc run -n extract -o data/raw.csv python extract.py

dvc run -n train -d data/raw.csv -o model.pkl python train.py

D. dvc add data/raw.csv
dvc add model.pkl

Solution

1. Step 1: Define extract stage with output only

   Extract stage produces data/raw.csv so it needs -n extract and -o data/raw.csv with the command.

2. Step 2: Define train stage depending on extract output

   Train stage depends on data/raw.csv so it needs -d data/raw.csv, outputs model.pkl, and runs python train.py.

3. Step 3: Confirm correct order and commands

   dvc stage add -n extract -o data/raw.csv python extract.py dvc stage add -n train -d data/raw.csv -o model.pkl python train.py correctly adds extract first, then train with proper dependencies and outputs.

4. Final Answer:

   dvc stage add -n extract -o data/raw.csv python extract.py dvc stage add -n train -d data/raw.csv -o model.pkl python train.py -> Option B

5. Quick Check:

   Define stages with correct deps and outputs [OK]

Hint: Add extract stage first, then train with dependency on extract output [OK]

Common Mistakes:

• Adding train stage before extract output exists
• Using dvc add instead of stage add for pipeline steps
• Missing dependencies in train stage