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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 capabilities
  • Application use cases in regulated industries

Establishing Enterprise MLOps Pipelines

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

Monitoring and Observability

  • Implementing live model monitoring and alert systems
  • Utilizing model performance dashboards
  • Hands-on lab: Configuring monitoring workflows

Grounding and Gen AI Evaluation

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

Compliance and Governance in Vertex AI

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

Scaling and Enterprise Integration

  • Scaling Vertex AI deployments effectively
  • Integrating with broader enterprise systems and APIs
  • Hands-on lab: Enterprise-scale deployment simulation

Case Studies and Best Practices

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

Summary and Next Steps

Requirements

  • Practical experience in deploying ML models to production environments.
  • Knowledge of CI/CD pipeline architectures.
  • Familiarity with data governance and compliance frameworks.

Target Audience

  • MLOps engineers
  • Platform teams
  • Compliance leads

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