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 Duration 14 hours

Course Outline

Introduction to Vertex AI for the Enterprise

  • Key enterprise AI requirements and associated challenges
  • Overview of Vertex AI's enterprise-specific features
  • Relevant use cases within regulated industries

Configuring Enterprise MLOps Pipelines

  • Seamlessly integrating Vertex AI with existing CI/CD workflows
  • Strategies for automation and orchestration
  • Practical lab: Constructing a robust deployment pipeline

Monitoring and Observability

  • Implementing live model monitoring and alerting systems
  • Developing comprehensive model performance dashboards
  • Practical lab: Establishing effective monitoring workflows

Grounding and Gen AI Evaluation

  • Anchoring models using enterprise data for grounding
  • Utilizing specialized Gen AI evaluation libraries and tools
  • Practical lab: Deploying evaluation workflows in practice

Compliance and Governance in Vertex AI

  • Leveraging data residency and access control functionalities
  • Ensuring auditability and full traceability
  • Practical lab: Configuring and enforcing compliance policies

Scaling and Enterprise Integration

  • Techniques for scaling Vertex AI deployments
  • Integrating with broader enterprise systems and APIs
  • Practical lab: Executing an enterprise-scale deployment

Case Studies and Best Practices

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

Summary and Next Steps

Requirements

  • Proven experience in deploying machine learning models to production environments
  • Working knowledge of CI/CD pipeline architecture
  • Sound understanding of data governance standards and compliance frameworks

Target Audience

  • MLOps engineers
  • Platform engineering teams
  • Compliance and governance leads

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