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 Duration 14 hours

Course Outline

Overview of Vertex AI for Enterprise Use

  • Specific AI requirements and operational challenges in enterprise settings
  • Key features of Vertex AI for corporate applications
  • Relevant use cases in highly regulated industries

Configuring Enterprise MLOps Pipelines

  • Connecting Vertex AI with CI/CD workflows
  • Strategies for automation and task orchestration
  • Practical exercise: constructing a deployment pipeline

Monitoring and Observability Capabilities

  • Real-time model monitoring and alert configuration
  • Visualization of model performance through dashboards
  • Practical exercise: setting up monitoring workflows

Grounding and Evaluation of Generative AI

  • Applying grounding to models using enterprise data
  • Available libraries and tools for Generative AI assessment
  • Practical exercise: implementing evaluation workflows

Compliance and Governance in Vertex AI

  • Features for data residency and access management
  • Ensuring auditability and full traceability
  • Practical exercise: configuring compliance policies

Scaling and Enterprise Integration

  • Scaling strategies for Vertex AI deployments
  • Integration with broader enterprise systems and APIs
  • Practical exercise: enterprise-scale deployment

Case Studies and Industry Best Practices

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

Conclusion and Recommended Next Steps

Requirements

  • Prior experience in deploying ML models to production environments
  • Proficiency with CI/CD pipeline architectures
  • Working knowledge of data governance and compliance standards

Target Audience

  • MLOps engineers
  • Platform engineering teams
  • Compliance officers

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