Why CI/CD differs for ML vs software in MLOps - Visual Breakdown

Process Flow - Why CI/CD differs for ML vs software

Start: Code Commit

↓

Software CI/CD

↓

Build -> Test -> Deploy

↓

Production Software

↓

ML CI/CD

↓

Data Collection -> Model Training

↓

Model Validation -> Deployment

↓

Production Model & Monitoring

Shows two parallel flows: software CI/CD is code-focused with build-test-deploy, while ML CI/CD adds data and model training steps before deployment.

Execution Sample

MLOps

# Software CI/CD steps
code_commit()
build()
test()
deploy()

# ML CI/CD steps
collect_data()
train_model()
validate_model()
deploy_model()

This code outlines the main steps in software CI/CD versus ML CI/CD pipelines.

Process Table

Step	Process	Action	Focus	Outcome
1	Software CI/CD	code_commit()	Code changes	Trigger pipeline
2	Software CI/CD	build()	Compile code	Build artifact
3	Software CI/CD	test()	Run tests	Verify code correctness
4	Software CI/CD	deploy()	Deploy app	Release software
5	ML CI/CD	collect_data()	Gather data	Prepare dataset
6	ML CI/CD	train_model()	Train model	Create model artifact
7	ML CI/CD	validate_model()	Evaluate model	Check model quality
8	ML CI/CD	deploy_model()	Deploy model	Release model to production
9	ML CI/CD	monitor_model()	Monitor performance	Detect model drift
10	End	Condition	All steps done	Pipeline complete

💡 All pipeline steps completed for both software and ML CI/CD

Status Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	After Step 4	After Step 5	After Step 6	After Step 7	After Step 8	After Step 9	Final
Code	Initial code	Committed	Built	Tested	Deployed	Deployed	Deployed	Deployed	Deployed	Deployed	Deployed
Data	Raw data	Raw data	Raw data	Raw data	Raw data	Collected	Collected	Collected	Collected	Collected	Collected
Model	None	None	None	None	None	None	Trained	Validated	Deployed	Monitored	Monitored

Key Moments - 3 Insights

Why does ML CI/CD include data collection and model training steps unlike software CI/CD?

ML CI/CD pipelines must handle data and model lifecycle, not just code. Execution table rows 5 and 6 show data collection and model training steps unique to ML.

Why is model validation a separate step in ML CI/CD?

Model validation ensures the model works well before deployment, unlike software tests which run earlier. See execution table row 7 for this distinct step.

Why is monitoring important after ML model deployment but not always shown in software CI/CD?

ML models can degrade over time due to data changes, so monitoring (row 9) detects this drift, which is less common in traditional software.

Visual Quiz - 3 Questions

Look at the execution table, at which step does the ML pipeline first produce a model artifact?

A Step 5

B Step 7

C Step 6

D Step 8

💡 Hint

Check the 'Action' and 'Outcome' columns for model creation in ML CI/CD steps.

According to the variable tracker, what is the state of 'Model' after step 7?

A None

B Validated

C Trained

D Deployed

💡 Hint

Look at the 'Model' row under 'After Step 7' in the variable tracker.

If monitoring was removed from the ML pipeline, which step would be missing from the execution table?

A Step 9

B Step 7

C Step 8

D Step 6

💡 Hint

Look for the step related to monitoring in the execution table.

Concept Snapshot

CI/CD for software focuses on code build, test, and deploy.
ML CI/CD adds data collection, model training, validation, and monitoring.
ML pipelines handle data and model lifecycle, not just code.
Monitoring is critical for ML to detect model drift.
Each step ensures quality before production release.

Full Transcript

This visual execution compares CI/CD pipelines for software and machine learning. Software CI/CD runs code commit, build, test, and deploy steps focused on code changes. ML CI/CD includes extra steps: collecting data, training a model, validating it, deploying, and monitoring performance. Variables like code, data, and model change state at different steps. Key differences are the handling of data and models in ML pipelines and the need for monitoring after deployment to catch model drift. The execution table and variable tracker show these steps clearly, helping beginners see why ML CI/CD is more complex than traditional software CI/CD.

Practice

1. Why does CI/CD for machine learning (ML) projects differ from traditional software CI/CD?

easy

A. Because ML CI/CD must handle data and model versioning in addition to code

B. Because ML CI/CD only focuses on code compilation

C. Because ML CI/CD does not require testing

D. Because ML CI/CD pipelines are simpler than software pipelines

Solution

1. Step 1: Understand the components of ML projects

   ML projects include data, models, and code, unlike traditional software which mainly involves code.

2. Step 2: Recognize CI/CD needs for ML

   ML CI/CD pipelines must manage data versioning and model validation along with code deployment.

3. Final Answer:

   Because ML CI/CD must handle data and model versioning in addition to code -> Option A

4. Quick Check:

   ML CI/CD = data + model + code handling [OK]

Hint: Remember ML needs data and model steps, not just code [OK]

Common Mistakes:

• Thinking ML CI/CD is only about code
• Ignoring data versioning in ML pipelines
• Assuming ML pipelines are simpler

2. Which of the following is a correct step unique to ML CI/CD pipelines compared to traditional software CI/CD?

easy

A. Compiling source code into binaries

B. Running unit tests on functions

C. Deploying web servers

D. Validating model accuracy on new data

Solution

1. Step 1: Identify unique ML pipeline steps

   ML pipelines include model validation steps to ensure model quality on new data.

2. Step 2: Compare with traditional software steps

   Traditional software CI/CD focuses on compiling code, testing, and deployment but not model validation.

3. Final Answer:

   Validating model accuracy on new data -> Option D

4. Quick Check:

   Model validation = ML CI/CD unique step [OK]

Hint: Look for model-specific validation steps [OK]

Common Mistakes:

• Confusing code compilation with ML-specific steps
• Ignoring model accuracy checks
• Assuming deployment steps are unique to ML

3. Consider this simplified ML CI/CD pipeline snippet:

steps:
  - name: Data Validation
    run: python validate_data.py
  - name: Train Model
    run: python train.py
  - name: Test Model
    run: python test_model.py
  - name: Deploy Model
    run: python deploy.py

What is the main reason for including the 'Data Validation' step in ML CI/CD?

medium

A. To deploy the model to production

B. To check if the training code has syntax errors

C. To ensure the input data meets quality standards before training

D. To compile the model into an executable

Solution

1. Step 1: Understand the purpose of data validation

   Data validation checks if input data is clean, complete, and correct before training.

2. Step 2: Relate data validation to ML pipeline quality

   Valid data is crucial for training accurate models; bad data causes poor results.

3. Final Answer:

   To ensure the input data meets quality standards before training -> Option C

4. Quick Check:

   Data validation = input data quality check [OK]

Hint: Data validation checks input quality before training [OK]

Common Mistakes:

• Confusing data validation with code syntax checks
• Thinking deployment happens before training
• Assuming model compilation is needed

4. You have an ML CI/CD pipeline that fails because the deployed model performs poorly after deployment. Which of these is the most likely cause related to ML CI/CD differences?

medium

A. The pipeline skipped retraining the model with updated data

B. The source code had a syntax error

C. The deployment server was offline

D. The unit tests for code functions failed

Solution

1. Step 1: Identify ML-specific pipeline failure causes

   ML models need retraining with new data to maintain accuracy over time.

2. Step 2: Analyze why skipping retraining affects model performance

   Without retraining, the model becomes outdated and performs poorly on new data.

3. Final Answer:

   The pipeline skipped retraining the model with updated data -> Option A

4. Quick Check:

   Model retraining skipped = poor deployed model [OK]

Hint: Check if model retraining step was missed [OK]

Common Mistakes:

• Blaming code syntax errors for model accuracy issues
• Ignoring data drift and retraining needs
• Assuming deployment server issues cause poor model

5. In an ML CI/CD pipeline, which combination of steps best ensures the model remains accurate and reliable after deployment?

hard

A. Code linting, unit tests, and container deployment

B. Data validation, model retraining, and performance monitoring

C. Static code analysis, integration tests, and server provisioning

D. Manual code review, manual testing, and manual deployment

Solution

1. Step 1: Identify key ML CI/CD steps for model quality

   Data validation ensures input quality, retraining updates the model, and monitoring tracks performance.

2. Step 2: Compare with traditional software steps

   Traditional steps like linting and unit tests do not cover data or model quality in ML.

3. Final Answer:

   Data validation, model retraining, and performance monitoring -> Option B

4. Quick Check:

   ML pipeline = data + retrain + monitor [OK]

Hint: Combine data checks, retraining, and monitoring for ML CI/CD [OK]

Common Mistakes:

• Choosing only code-focused steps ignoring data/model
• Assuming manual steps ensure ML model accuracy
• Confusing software CI/CD with ML CI/CD needs