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 Duration 14 hours

Course Outline

Introduction to Vertex AI in an Enterprise Context

  • Enterprise AI requirements and associated challenges
  • Overview of Vertex AI’s enterprise capabilities
  • Use cases within regulated industries

Establishing Enterprise MLOps Pipelines

  • Integrating Vertex AI with CI/CD workflows
  • Automation and orchestration techniques
  • Hands-on lab: Constructing a deployment pipeline

Monitoring and Observability

  • Live model monitoring and alerting mechanisms
  • Model performance dashboards
  • Hands-on lab: Configuring monitoring workflows

Grounding and Gen AI Evaluation

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

Compliance and Governance within Vertex AI

  • Data residency and access control features
  • Auditability and traceability
  • Hands-on lab: Setting up compliance policies

Scaling and Enterprise Integration

  • Scaling Vertex AI deployments
  • Integration with enterprise systems and APIs
  • Hands-on lab: Enterprise-scale deployment

Case Studies and Best Practices

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

Summary and Next Steps

Requirements

  • Proven experience deploying ML models in production environments.
  • Proficiency with CI/CD pipelines.
  • A solid grasp of data governance and compliance frameworks.

Target Audience

  • MLOps engineers.
  • Platform teams.
  • Compliance leads.

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