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 Duration 14 hours

Course Outline

Introduction to Vertex AI for the Enterprise

  • Enterprise AI requirements and associated challenges
  • Overview of Vertex AI enterprise capabilities
  • Application of use cases in regulated industries

Establishing Enterprise MLOps Pipelines

  • Integration of Vertex AI with CI/CD workflows
  • Automation and orchestration strategies
  • Practical lab: Constructing a deployment pipeline

Monitoring and Observability

  • Live model monitoring and alerting mechanisms
  • Model performance dashboards and metrics
  • Practical lab: Configuring monitoring workflows

Grounding and Gen AI Evaluation

  • Grounding models with enterprise-specific data
  • Utilising Gen AI evaluation libraries and tools
  • Practical lab: Implementing evaluation workflows

Compliance and Governance in Vertex AI

  • Data residency and access control features
  • Auditability and traceability considerations
  • Practical lab: Configuring compliance policies

Scaling and Enterprise Integration

  • Strategies for scaling Vertex AI deployments
  • Integration with enterprise systems and APIs
  • Practical lab: Enterprise-scale deployment scenarios

Case Studies and Best Practices

  • Success stories from financial services, healthcare, and the public sector
  • Lessons learned from enterprise adoption experiences
  • Best practices for sustained long-term operations

Summary and Next Steps

Requirements

  • Practical experience in deploying ML models to production environments
  • Familiarity with CI/CD pipelines
  • Working knowledge of data governance and compliance frameworks

Target Audience

  • MLOps engineers
  • Platform teams
  • Compliance leads

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Provisional Upcoming Courses (Require 5+ participants)

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