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 Duration 14 hours

Course Outline

Introduction to Vertex AI in Enterprise Settings

  • Key enterprise AI requirements and associated challenges
  • Overview of Vertex AI’s enterprise features
  • Application use cases within regulated industries

Establishing Enterprise MLOps Pipelines

  • Integrating Vertex AI with CI/CD workflows
  • Automation and orchestration techniques
  • Practical lab: constructing a deployment pipeline

Monitoring and Observability Practices

  • Live model monitoring and alerting mechanisms
  • Creating model performance dashboards
  • Practical lab: setting up monitoring workflows

Grounding and Evaluating Generative AI

  • Grounding models using enterprise data
  • Utilizing Generative AI evaluation libraries and tools
  • Practical lab: implementing evaluation workflows

Compliance and Governance within Vertex AI

  • Data residency features and access control mechanisms
  • Ensuring auditability and traceability
  • Practical lab: configuring compliance policies

Scaling and Enterprise Integration

  • Scaling Vertex AI deployments
  • Integration with broader enterprise systems and APIs
  • Practical lab: executing enterprise-scale deployments

Case Studies and Industry Best Practices

  • Success stories from the financial services, healthcare, and public sectors
  • Insights gained from enterprise adoption experiences
  • Best practices for sustaining long-term operations

Summary and Recommended Next Steps

Requirements

  • Practical experience in deploying ML models to production environments
  • Familiarity with CI/CD pipeline workflows
  • Understanding of data governance and compliance frameworks

Target Audience

  • MLOps engineers
  • Platform teams
  • Compliance leads

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