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 Duration 14 hours

Course Outline

Introduction to Vertex AI for the Enterprise

  • Key enterprise AI requirements and associated challenges
  • Overview of Vertex AI's enterprise-centric features
  • Application examples within regulated industries

Building Enterprise MLOps Pipelines

  • Integration of Vertex AI into CI/CD workflows
  • Techniques for automation and orchestration
  • Practical lab: Constructing a deployment pipeline

Monitoring and Observability

  • Implementing live model monitoring and alerting systems
  • Developing model performance dashboards
  • Practical lab: Configuring monitoring workflows

Grounding and Gen AI Evaluation

  • Grounding models using enterprise data sources
  • Leveraging Gen AI evaluation libraries and tools
  • Practical lab: Executing evaluation workflows

Compliance and Governance with Vertex AI

  • Features for data residency and access control
  • Ensuring auditability and traceability
  • Practical lab: Setting up compliance policies

Scaling and Enterprise Integration

  • Methods for scaling Vertex AI deployments
  • Integration with broader enterprise systems and APIs
  • Practical lab: Executing an enterprise-scale deployment

Case Studies and Best Practices

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

Conclusion and Recommended Next Steps

Requirements

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

Target Audience

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

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