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Course Outline

Introduction to Vertex AI for the Enterprise

  • Enterprise AI requirements and key challenges
  • Overview of Vertex AI enterprise capabilities
  • Application in regulated industries

Configuring Enterprise MLOps Pipelines

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

Monitoring and Observability

  • Real-time model monitoring and alerting systems
  • Performance dashboards for models
  • Practical lab: establishing monitoring workflows

Grounding and Generative AI Evaluation

  • Anchoring models with enterprise data
  • Generative AI evaluation libraries and tools
  • Practical lab: executing evaluation workflows

Compliance and Governance in Vertex AI

  • Data residency and access control mechanisms
  • Auditability and traceability features
  • Practical lab: setting up compliance policies

Scaling and Enterprise Integration

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

Case Studies and Best Practices

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

Summary and Next Steps

Requirements

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

Target Audience

  • MLOps engineers
  • Platform teams
  • Compliance leads
 14 Hours

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