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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-specific features
  • Application scenarios in regulated industries

Establishing Enterprise MLOps Pipelines

  • Integrating Vertex AI into CI/CD workflows
  • Strategies for automation and orchestration
  • Hands-on lab: Constructing a deployment pipeline

Monitoring and Observability

  • Implementing live model monitoring and alerting systems
  • Creating model performance dashboards
  • Hands-on lab: Configuring monitoring workflows

Grounding and Gen AI Evaluation

  • Grounding models using enterprise data sources
  • Utilizing Gen AI evaluation libraries and tools
  • Hands-on lab: Setting up evaluation workflows

Compliance and Governance in Vertex AI

  • Data residency features and access control mechanisms
  • Ensuring auditability and traceability
  • Hands-on lab: Configuring compliance policies

Scaling and Enterprise Integration

  • Scaling Vertex AI deployments for high volume
  • Integrating with broader enterprise systems and APIs
  • Hands-on lab: Executing enterprise-scale deployment

Case Studies and Best Practices

  • Success stories from financial services, healthcare, and the public sector
  • Insights gained from enterprise adoption experiences
  • Best practices for sustaining long-term operations

Summary and Future Steps

Requirements

  • 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 teams
  • Compliance leads

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