End-to-End MLOps with Azure Machine Learning and GitHub Actions
Implement approval gates before production deployment. Track model lineage, performance metrics, and data dependencies for full auditability.
14 articles
Implement approval gates before production deployment. Track model lineage, performance metrics, and data dependencies for full auditability.
Feature Azure DevOps GitHub Actions YAML Config azure pipelines.yml .github/workflows/ .yml Hosted Runners Microsoft hosted GitHub hosted Self hosted Yes…
OIDC eliminates the need for long-lived cloud credentials, improving security posture.
Proper artifact management enables efficient multi-job pipelines and deployment workflows.
Proper caching can reduce build times by 50-80% for dependency-heavy projects.
Custom actions enable sophisticated automation tailored to your specific workflows.
Composite actions bridge the gap between simple step sequences and full custom actions.
Reusable workflows reduce duplication and improve maintainability across repositories.
Required workflows ensure consistent security and quality standards across the enterprise.
Larger runners can dramatically reduce build times when used effectively with parallelizable workloads.
GitHub now offers larger hosted runners for more demanding workloads. Enforce workflows across all repositories in an organization.
ML CI/CD in 2021 became essential for production systems. The tooling caught up with the need, and now there's no excuse for manual deployments.
GitHub Actions workflows for Azure deployment have become my default for any project that lives on GitHub and targets Azure. The Azure/login action with…
GitHub Actions went from "interesting alternative to Azure DevOps Pipelines" to "my default CI/CD choice for most new Azure projects" in about twelve…