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 Duration 14 hours

Course Outline

Introduction to Enterprise Vertex AI

  • Specific AI requirements and challenges in enterprise settings.
  • Overview of enterprise-specific features in Vertex AI.
  • Application use cases within highly regulated industries.

Configuring Enterprise MLOps Pipelines

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

Monitoring and Observability Practices

  • Implementing live model monitoring and alerting mechanisms.
  • Utilizing model performance dashboards.
  • Practical exercise: establishing monitoring workflows.

Grounding and Evaluating Generative AI

  • Applying grounding techniques using enterprise datasets.
  • Review of evaluation libraries and tools for generative AI.
  • Practical exercise: implementing evaluation workflows.

Compliance and Governance in Vertex AI

  • Features for data residency and access control.
  • Ensuring auditability and traceability.
  • Practical exercise: setting up compliance policies.

Scaling and Enterprise System Integration

  • Techniques for scaling Vertex AI deployments.
  • Connecting with enterprise systems and APIs.
  • Practical exercise: deploying at enterprise scale.

Case Studies and Industry Best Practices

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

Conclusions and Future Pathways

Requirements

  • Practical experience in deploying machine learning models to production environments.
  • Working knowledge of CI/CD pipeline mechanisms.
  • Comprehensive understanding of data governance standards and compliance frameworks.

Target Audience

  • MLOps Engineers
  • Platform Engineering Teams
  • Compliance and Governance Officers

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