GitHub Actions for ML CI Pipelines
Automate Testing, Training, Evaluation, and Deployment to Build Reliable Machine Learning Systems.
By Sandip Das · · 26 min read
💡
Hey — It's Sandip Das 👋
Learning GitHub Actions helps us automate testing, training, and evaluation so every change is checked before deployment.
It reduces manual work, catches issues early, and makes ML releases more consistent and reliable.
Learning GitHub Actions helps us automate testing, training, and evaluation so every change is checked before deployment.
It reduces manual work, catches issues early, and makes ML releases more consistent and reliable.
📚 Key Learnings
- The five words that are all of GitHub Actions
- Your first workflow file
- The YAML surface you actually use
- Triggers: what makes a workflow run
- Runners: where the work happens
- Contexts, expressions and conditionals
- The repository we are wiring up
- The CI workflow, built in three passes
- Caching, matrices and artifacts
- Turning a green check into a gate
- Debugging a workflow
🧠 Learn here
On Day 31 we talked about triggers, stages, jobs, artifacts, and the four families of tests that an ML repository nee
Read the full article on learnxops.com.
Topics: 100 Days of MLOps, MLOps, ML, GitHub Actions