Feb 18
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Build your first automated CI pipeline from scratch using GitHub Actions. Master YAML syntax and learn how to trigger workflows on every code push. Stop guessing how automation works. In Video 2 of our MLOps series, we build a 100% working Continuous Integration pipeline—the foundation of all modern AI engineering. We strip away the complexity of Docker and Python to focus purely on the orchestration logic that powers the world’s biggest tech companies. What You Will Build & Learn: The .yml Blueprint: Exactly where to place your .github/workflows/ci.yml and why the folder structure is non-negotiable. The CI Anatomy: A deep dive into the Trigger → Workflow → Job → Steps hierarchy. Ephemeral Runners: Understanding how GitHub spins up a "fresh machine" for every single run to ensure consistency. YAML Essentials: The only syntax you actually need to know to start automating your MLOps tasks. The Proof of Concept: Using simple echo commands to verify your "Robot" is alive before we add complex ML code. Why this matters for your career: Junior developers manually test code; Senior MLOps Engineers write YAML. This video gives you the fundamental skill needed to manage complex ML pipelines, secrets, and production deployments. #GitHubActions #CICD #MLOps #DevOps #YAML #Automation #SoftwareEngineering #Python #GithubTutorial #AIEngineering