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Course Outline

Introduction to Vertex AI for the Enterprise

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

Setting Up Enterprise MLOps Pipelines

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

Monitoring and Observability

  • Real-time model monitoring and alerting
  • 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 in Vertex AI

  • Data residency and access control features
  • Auditability and traceability
  • Hands-on lab: Configuring 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 learned from enterprise adoption
  • Best practices for long-term operational success

Summary and Next Steps

Requirements

  • Experience deploying ML models in production environments
  • Familiarity with CI/CD pipelines
  • Knowledge of data governance and compliance frameworks

Target Audience

  • MLops engineers
  • Platform engineering teams
  • Compliance leads
 14 Hours

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