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 Duration 14 hours

Course Outline

Introduction to Vertex AI in an Enterprise Context

  • Key enterprise AI requirements and associated challenges
  • Overview of Vertex AI’s enterprise-focused features
  • Application scenarios in highly regulated industries

Building Enterprise-Grade MLOps Pipelines

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

Monitoring and Observability

  • Implementing live model monitoring and alerting mechanisms
  • Utilizing model performance dashboards for visibility
  • Hands-on lab: Establishing comprehensive monitoring workflows

Grounding and Gen AI Evaluation

  • Grounding models using proprietary enterprise data
  • Leveraging Gen AI evaluation libraries and specialized tools
  • Hands-on lab: Implementing structured evaluation workflows

Compliance and Governance in Vertex AI

  • Configuring data residency and fine-grained access control features
  • Ensuring auditability and traceability of actions
  • Hands-on lab: Configuring and managing compliance policies

Scaling and Enterprise Integration

  • Scalability strategies for Vertex AI deployments
  • Integration with existing enterprise systems and APIs
  • Hands-on lab: Executing an enterprise-scale deployment

Case Studies and Industry Best Practices

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

Summary and Recommended Next Steps

Requirements

  • Proven experience in deploying ML models to production environments
  • Solid working knowledge of CI/CD pipelines
  • A clear understanding of data governance principles and compliance frameworks

Target Audience

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

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