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Course Outline

Introduction to Enterprise Vertex AI

  • Enterprise AI requirements and challenges
  • Overview of Vertex AI enterprise features
  • Use cases in regulated industries

Establishing Enterprise MLOps Pipelines

  • Integrating Vertex AI with CI/CD workflows
  • Automation and orchestration strategies
  • Hands-on lab: Constructing a deployment pipeline

Monitoring and Observability

  • Real-time model monitoring and alerting mechanisms
  • Model performance dashboards
  • Hands-on lab: Configuring monitoring workflows

Grounding and Gen AI Evaluation

  • Grounding models with enterprise data
  • Gen AI evaluation libraries and tools
  • Hands-on lab: Implementing evaluation workflows

Compliance and Governance in Vertex AI

  • Data residency and access control features
  • Auditability and traceability
  • Hands-on lab: Configuring compliance policies

Scaling and Enterprise Integration

  • Scaling Vertex AI deployments
  • Integration with enterprise systems and APIs
  • Hands-on lab: Executing enterprise-scale deployment

Case Studies and Best Practices

  • Success stories from financial services, healthcare, and the public sector
  • Key lessons learned from enterprise adoption efforts
  • Best practices for sustained operational excellence

Summary and Next Steps

Requirements

  • Experience in deploying ML models in production environments
  • Proficiency with CI/CD pipelines
  • Understanding of data governance and compliance frameworks

Target Audience

  • MLOps Engineers
  • Platform Engineering Teams
  • Compliance Leads
 14 Hours

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