Apr 24
Loading video player...
In this video, I walk you through deploying a complete MLOps solution on Azure. By the end of this video you'll have: - Azure ML workspace provisioned with Bicep (IaC) - MLflow experiment tracking with auto model registration - Reusable pipeline components with step caching - Managed online endpoint for real-time predictions - GitHub Actions CI/CD with OIDC (no stored secrets) ───────────────────────── TIMESTAMPS ───────────────────────── 0:00 - Introduction & architecture overview 1:35 - Prerequisites 1:55 - Part 1: OIDC Auth 2:55 - Part 2: Infrastructure as Code 4:45 - Part 3: Infrastructure Workflow 6:55 - Part 4: ML Code & Components 10:55 - Part 5: ML Pipeline workflow 16:30 - Part 6: GitHub Actions & Azure validation 18:45 - Outro ───────────────────────── CONNECT WITH ME: ───────────────────────── - GitHub: [https://github.com/brayaON] - Twitter/X: [https://x.com/brayaON20] - LinkedIn: [https://www.linkedin.com/in/bof23402] - Website: [https://boflabs.dev/] #azureai #bicep #machinelearning #infrastructureascode #iac #mlops #azureopenai #microsoftazure #learnazure #devops #githubactions #bicep #mlflow #cloudcomputing #datascience #ai #python #ci_cd