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Duration 14 hours
Course Outline
Introduction to Enterprise Vertex AI
- Key requirements and challenges in enterprise AI
- Overview of Vertex AI enterprise features
- Application use cases in regulated industries
Establishing Enterprise MLOps Pipelines
- Integrating Vertex AI with CI/CD workflows
- Workflow automation and orchestration
- Practical lab: constructing a deployment pipeline
Monitoring and Observability
- Real-time model monitoring and alerting systems
- Model performance dashboards
- Practical lab: configuring monitoring workflows
Grounding and Gen AI Evaluation
- Anchoring models with enterprise data
- Gen AI assessment libraries and utilities
- Practical lab: implementing evaluation workflows
Compliance and Governance in Vertex AI
- Data residency and access control capabilities
- Ensuring auditability and traceability
- Practical lab: setting up compliance policies
Scaling and Enterprise Integration
- Scaling Vertex AI deployments
- Integration with enterprise systems and APIs
- Practical lab: enterprise-scale deployment
Case Studies and Best Practices
- Success stories from financial services, healthcare, and the public sector
- Insights from enterprise adoption journeys
- Best practices for sustained long-term operations
Summary and Future Pathways
Requirements
- Practical experience in deploying ML models to production environments
- Proficiency with CI/CD pipelines
- Knowledge of data governance and compliance frameworks
Target Audience
- MLOps engineers
- Platform teams
- Compliance leads
Testimonials (1)
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