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Course Outline

Vertex AI for Enterprise: An Introduction

  • Key enterprise AI requirements and associated challenges
  • Overview of Vertex AI’s enterprise-oriented features
  • Applications in heavily regulated sectors

Building Enterprise MLOps Pipelines

  • Integrating Vertex AI into CI/CD workflows
  • Strategies for automation and orchestration
  • Practical session: Constructing a deployment pipeline

Monitoring and Observability

  • Real-time model monitoring and alerting mechanisms
  • Utilizing model performance dashboards
  • Practical session: Establishing monitoring workflows

Grounding and Gen AI Evaluation

  • Anchoring models with enterprise-specific data
  • Exploring Gen AI evaluation libraries and utilities
  • Practical session: Executing evaluation workflows

Compliance and Governance in Vertex AI

  • Managing data residency and access control features
  • Ensuring auditability and traceability
  • Practical session: Configuring compliance policies

Scaling and Enterprise Integration

  • Methods for scaling Vertex AI deployments
  • Connecting with enterprise systems and APIs
  • Practical session: Enterprise-scale deployment scenarios

Case Studies and Industry Best Practices

  • Success narratives in finance, healthcare, and public services
  • Key takeaways from enterprise adoption experiences
  • Best practices for sustained long-term operations

Conclusion and Future Directions

Requirements

  • Practical experience in deploying ML models to production environments
  • Knowledge of CI/CD pipeline mechanisms
  • Conceptual grasp of data governance and compliance frameworks

Target Audience

  • MLOps engineers
  • Platform engineering teams
  • Compliance officers
 14 Hours

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