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 Duration 14 hours

Course Outline

Introduction to Vertex AI for Enterprise Environments

  • Enterprise AI needs and associated challenges
  • Overview of Vertex AI's enterprise capabilities
  • Application in regulated industries

Establishing Enterprise MLOps Pipelines

  • Connecting Vertex AI with CI/CD workflows
  • Process automation and orchestration
  • Practical lab: Constructing a deployment pipeline

Monitoring and Observability

  • Real-time model monitoring and alert mechanisms
  • Performance analytics and dashboards
  • Practical lab: Configuring monitoring workflows

Grounding and Generative AI Evaluation

  • Grounding models using enterprise datasets
  • Generative AI evaluation frameworks and libraries
  • Practical lab: Implementing evaluation workflows

Compliance and Governance in Vertex AI

  • Data residency and access control mechanisms
  • Audit trails and traceability
  • 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 Industry Best Practices

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

Summary and Next Steps

Requirements

  • Practical experience in deploying ML models to production environments
  • Proficiency with CI/CD pipelines
  • A solid grasp of data governance and compliance frameworks

Target Audience

  • MLOps Engineers
  • Platform Teams
  • Compliance Officers

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