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Duration 14 hours
Course Outline
Introduction to Vertex AI for Enterprise Environments
- Key AI requirements and operational challenges in enterprise settings
- Overview of Vertex AI’s enterprise-specific capabilities
- Application cases within regulated industries
Configuring Enterprise MLOps Pipelines
- Integration of Vertex AI with CI/CD workflows
- Automation strategies and orchestration techniques
- Practical exercise: constructing a deployment pipeline
Monitoring and System Observability
- Real-time model monitoring and alerting mechanisms
- Design and interpretation of model performance dashboards
- Practical exercise: implementing monitoring workflows
Grounding and Gen AI Assessment
- Grounding models using proprietary enterprise data
- Exploring Gen AI evaluation libraries and auxiliary tools
- Practical exercise: executing evaluation workflows
Compliance and Governance within Vertex AI
- Features for data residency and access control
- Ensuring auditability and full traceability
- Practical exercise: setting up compliance policies
Scalability and Enterprise Integration
- Scaling strategies for Vertex AI deployments
- Integration with existing enterprise systems and APIs
- Practical exercise: managing enterprise-scale deployments
Case Studies and Operational Best Practices
- Success narratives in financial services, healthcare, and public sectors
- Key takeaways from enterprise adoption experiences
- Recommended practices for sustained long-term operations
Conclusion and Future Roadmap
Requirements
- Hands-on experience in deploying ML models to production environments
- Working knowledge of CI/CD pipelines
- Familiarity with data governance and compliance standards
Target Audience
- MLOps engineers
- Platform engineering teams
- Compliance and governance leads
Testimonials (1)
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