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 Duration 14 hours

Course Outline

Introduction to Vertex AI in an Enterprise Context

  • Key AI requirements and challenges faced by enterprises
  • Overview of Vertex AI’s enterprise-specific features
  • Application scenarios in highly regulated sectors

Establishing Enterprise MLOps Pipelines

  • Connecting Vertex AI with CI/CD workflows
  • Techniques for automation and orchestration
  • Practical exercise: Constructing a deployment pipeline

Monitoring and Observability Practices

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

Grounding and Gen AI Assessment

  • Grounding models using enterprise datasets
  • Exploration of Gen AI evaluation libraries and utilities
  • Practical exercise: Executing evaluation workflows

Compliance and Governance with Vertex AI

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

Scaling and Enterprise-System Integration

  • Strategies for scaling Vertex AI deployments
  • Integration with internal enterprise systems and APIs
  • Practical exercise: Deploying at enterprise scale

Case Studies and Industry Best Practices

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

Conclusion and Future Recommendations

Requirements

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

Target Audience

  • MLOps engineers
  • Platform engineering teams
  • Compliance officers

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