Why CI/CD differs for ML vs software in MLOps - Why It Works

Introduction

CI/CD helps deliver software updates fast and safely. For machine learning, CI/CD must handle extra steps like training models and managing data, which makes it different from regular software delivery.

When you want to automate retraining and deployment of machine learning models after new data arrives

When you need to test both code and model quality before releasing updates

When you want to track model versions alongside code changes

When you must deploy models to production environments reliably and repeatedly

When you want to monitor model performance and trigger updates automatically

Commands

Clone the machine learning project repository to get the latest code and model files.

Terminal

git clone https://github.com/example/ml-project.git

Expected Output

Cloning into 'ml-project'...

remote: Enumerating objects: 50, done.

remote: Counting objects: 100% (50/50), done.

remote: Compressing objects: 100% (30/30), done.

remote: Total 50 (delta 10), reused 40 (delta 5), pack-reused 0

Receiving objects: 100% (50/50), 5.0 MiB | 2.0 MiB/s, done.

Resolving deltas: 100% (10/10), done.

Train the machine learning model using the latest training data and save the trained model file.

Terminal

python train_model.py --data data/training.csv --output models/model_v1.pkl

Expected Output

Loading data from data/training.csv

Training model...

Model training complete.

Model saved to models/model_v1.pkl

→

--data - Specifies the path to the training data file

→

--output - Specifies where to save the trained model file

Run tests to check the quality and accuracy of the trained model before deployment.

Terminal

pytest tests/test_model_quality.py

Expected Output

============================= test session starts ==============================

collected 3 items



tests/test_model_quality.py ...                                          [100%]



============================== 3 passed in 1.23s ===============================

Deploy the new model version to the production environment using Kubernetes.

Terminal

kubectl apply -f deployment.yaml

Expected Output

deployment.apps/ml-model-deployment created

Check that the pods running the machine learning model are up and ready after deployment.

Terminal

kubectl get pods -l app=ml-model

Expected Output

NAME                               READY   STATUS    RESTARTS   AGE

ml-model-deployment-5f7d8f9d7f-abc12   1/1     Running   0          30s

→

-l app=ml-model - Filters pods by label to show only those related to the ML model

Key Concept

If you remember nothing else, remember: ML CI/CD must handle data, model training, testing, and deployment steps, unlike regular software CI/CD which focuses mainly on code.

Common Mistakes

Treating ML model files like regular code files in CI/CD pipelines

Model files are large and binary, requiring special handling for versioning and deployment.

Use dedicated model storage and versioning tools, and include model validation steps in the pipeline.

Skipping model quality tests before deployment

Deploying untested models can cause poor predictions and business impact.

Always run automated tests on model accuracy and performance before deploying.

Ignoring data changes in the CI/CD process

Model performance depends on data; ignoring data updates can cause stale models.

Include data validation and trigger retraining when new data arrives.

Summary

CI/CD for ML includes extra steps like training models and testing their quality.

You must handle data, model files, and code together in the pipeline.

Deployment involves updating running models safely and verifying their readiness.