CI/CD for ML pipelines in ML Python

Introduction

CI/CD for ML pipelines helps automate testing and delivery of machine learning models. It makes sure models are updated safely and quickly.

You want to automatically retrain and test ML models when new data arrives.

You need to deploy updated ML models to production without manual steps.

You want to catch errors early in your ML code or data before deployment.

You want to track changes in your ML code, data, and model versions.

You want to speed up collaboration between data scientists and engineers.

Syntax

ML Python

pipeline:
  stages:
    - name: train
      script: python train.py
    - name: test
      script: python test.py
    - name: deploy
      script: python deploy.py
  triggers:
    - on_push
    - on_schedule

This is a simple example of a CI/CD pipeline configuration for ML.

Each stage runs a script: training, testing, then deploying the model.

Examples

This example shows separate stages for training, validating, and deploying the ML model.

ML Python

stages:
  - train
  - validate
  - deploy

train:
  script: python train.py

validate:
  script: python validate.py

deploy:
  script: python deploy.py

This example triggers retraining, testing, and deployment when code is pushed.

ML Python

on_push:
  - run: python retrain.py
  - run: python test.py
  - run: python deploy.py

This example runs the pipeline every day at 2 AM automatically.

ML Python

schedule:
  cron: '0 2 * * *'
  jobs:
    - run: python retrain.py
    - run: python test.py
    - run: python deploy.py

Sample Model

This pipeline runs training, testing, and deployment scripts automatically when code is pushed.

ML Python

stages:
  - train
  - test
  - deploy

train:
  script: python train.py

test:
  script: python test.py

deploy:
  script: python deploy.py

triggers:
  - on_push

Important Notes

Always test your ML model before deploying to avoid errors in production.

Use version control for your data and models to track changes clearly.

Automate retraining when new data arrives to keep models accurate.

Summary

CI/CD pipelines automate ML model training, testing, and deployment.

They help deliver updates quickly and safely.

Use triggers like code pushes or schedules to run pipelines automatically.