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Course Outline

Introduction to Vertex AI in Enterprise Contexts

  • Core AI requirements and challenges in enterprise settings
  • Overview of Vertex AI’s enterprise-specific features
  • Application use cases within regulated industries

Configuring Enterprise MLOps Pipelines

  • Integrating Vertex AI with CI/CD workflows
  • Strategies for automation and orchestration
  • Practical lab: constructing a deployment pipeline

Monitoring and Observability Frameworks

  • Implementing live model monitoring and alerting systems
  • Utilizing model performance dashboards
  • Practical lab: configuring monitoring workflows

Grounding and Gen AI Evaluation Strategies

  • Anchoring models with enterprise-specific data
  • Exploring Gen AI evaluation libraries and tooling
  • Practical lab: executing evaluation workflows

Compliance and Governance in Vertex AI

  • Managing data residency and access control mechanisms
  • Ensuring auditability and traceability
  • Practical lab: setting up compliance policies

Scaling and Enterprise Integration

  • Strategies for scaling Vertex AI deployments
  • Integrating with broader enterprise systems and APIs
  • Practical lab: executing enterprise-scale deployments

Case Studies and Industry Best Practices

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

Summary and Recommended Next Steps

Requirements

  • Practical experience in deploying ML models to production environments
  • Proficiency with CI/CD pipeline workflows
  • Solid understanding of data governance and compliance frameworks

Target Audience

  • MLOps Engineers
  • Platform Engineering Teams
  • Compliance and Governance Leads
 14 Hours

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