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 Duration 14 hours

Course Outline

Introduction to Enterprise-Grade Vertex AI

  • Key requirements and challenges for enterprise AI
  • Overview of Vertex AI's enterprise-specific features
  • Application scenarios in highly regulated industries

Establishing Enterprise MLOps Pipelines

  • Integrating Vertex AI into CI/CD workflows
  • Strategies for automation and orchestration
  • Practical exercise: Creating a deployment pipeline

Monitoring and Observability

  • Implementing live model monitoring and alert systems
  • Designing model performance dashboards
  • Practical exercise: Configuring monitoring workflows

Grounding and Evaluating Gen AI

  • Grounding models using proprietary enterprise data
  • Overview of Gen AI evaluation libraries and tools
  • Practical exercise: Implementing evaluation workflows

Compliance and Governance within Vertex AI

  • Features for data residency and access control
  • Ensuring auditability and traceability
  • Practical exercise: Setting up compliance policies

Scaling and Enterprise Integration

  • Scaling Vertex AI deployments effectively
  • Integrating with broader enterprise systems and APIs
  • Practical exercise: Managing enterprise-scale deployments

Case Studies and Industry Best Practices

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

Summary and Future Directions

Requirements

  • Proven experience in deploying ML models into production environments
  • Working knowledge of CI/CD pipelines
  • Conceptual understanding of data governance and compliance frameworks

Target Audience

  • MLOps engineers
  • Platform engineering teams
  • Compliance leaders

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