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 Duration 14 hours

Course Outline

Introduction to Enterprise Vertex AI

  • Key requirements and challenges in enterprise AI
  • Overview of Vertex AI enterprise features
  • Application use cases in regulated industries

Establishing Enterprise MLOps Pipelines

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

Monitoring and Observability

  • Real-time model monitoring and alerting systems
  • Model performance dashboards
  • Practical lab: configuring monitoring workflows

Grounding and Gen AI Evaluation

  • Anchoring models with enterprise data
  • Gen AI assessment libraries and utilities
  • Practical lab: implementing evaluation workflows

Compliance and Governance in Vertex AI

  • Data residency and access control capabilities
  • Ensuring auditability 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 Best Practices

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

Summary and Future Pathways

Requirements

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

Target Audience

  • MLOps engineers
  • Platform teams
  • Compliance leads

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