CI/CD Fundamentals for Machine Learning
Automate Testing, Training, Evaluation, and Deployment to Build Reliable Machine Learning Systems.
By Sandip Das · · 28 min read
💡
Hey — It's Sandip Das 👋
We need CI/CD to automate code validation, model training, evaluation, and deployment while maintaining consistent quality across the ML lifecycle.
It enables faster, safer releases by detecting problems in code, data, and model performance before they reach production.
We need CI/CD to automate code validation, model training, evaluation, and deployment while maintaining consistent quality across the ML lifecycle.
It enables faster, safer releases by detecting problems in code, data, and model performance before they reach production.
📚 Key Learnings
- trigger, stage, job, artifact
- CI for code vs CI for ML
- The trigger taxonomy
- The reproducibility quadruple
- Four families of ML tests
- Designing the five-stage pipeline
- Implementation: repo layout
- Stage 1 & 2: lint and test
- Stage 3: train
- Stage 4: evaluate and the promotion gate
- Stage 5: deploy
- The complete workflow file
- The same pipeline in GitLab CI
- Runners, cost, and GPU stages
- Failure modes you will hit
🧠 Learn here
For thirty days you have been building the pieces. DVC gave you versioned data. MLflo
Read the full article on learnxops.com.
Topics: 100 Days of MLOps, MLOps, ML, Kubernetes, Kubeflow