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 Duration 14 hours

Course Outline

Introduction to Vertex AI for Enterprise Environments

  • Key AI requirements and operational challenges in enterprise settings
  • Overview of Vertex AI’s enterprise-specific capabilities
  • Application cases within regulated industries

Configuring Enterprise MLOps Pipelines

  • Integration of Vertex AI with CI/CD workflows
  • Automation strategies and orchestration techniques
  • Practical exercise: constructing a deployment pipeline

Monitoring and System Observability

  • Real-time model monitoring and alerting mechanisms
  • Design and interpretation of model performance dashboards
  • Practical exercise: implementing monitoring workflows

Grounding and Gen AI Assessment

  • Grounding models using proprietary enterprise data
  • Exploring Gen AI evaluation libraries and auxiliary tools
  • Practical exercise: executing evaluation workflows

Compliance and Governance within Vertex AI

  • Features for data residency and access control
  • Ensuring auditability and full traceability
  • Practical exercise: setting up compliance policies

Scalability and Enterprise Integration

  • Scaling strategies for Vertex AI deployments
  • Integration with existing enterprise systems and APIs
  • Practical exercise: managing enterprise-scale deployments

Case Studies and Operational Best Practices

  • Success narratives in financial services, healthcare, and public sectors
  • Key takeaways from enterprise adoption experiences
  • Recommended practices for sustained long-term operations

Conclusion and Future Roadmap

Requirements

  • Hands-on experience in deploying ML models to production environments
  • Working knowledge of CI/CD pipelines
  • Familiarity with data governance and compliance standards

Target Audience

  • MLOps engineers
  • Platform engineering teams
  • Compliance and governance leads

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