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 Duration 14 hours

Course Outline

Introduction to Vertex AI for the Enterprise

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

Configuring Enterprise MLOps Pipelines

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

Monitoring and Observability

  • Real-time model monitoring and alerting mechanisms
  • Model performance dashboards
  • Practical lab: Implementing monitoring workflows

Grounding and Gen AI Evaluation

  • Grounding models with enterprise data
  • Gen AI evaluation libraries and tools
  • Practical lab: Executing evaluation workflows

Compliance and Governance in Vertex AI

  • Data residency and access control functionalities
  • 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 gained from enterprise adoption
  • Best practices for long-term operations

Summary and Next Steps

Requirements

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

Target Audience

  • MLOps engineers
  • Platform teams
  • Compliance leads

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