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Duration 14 hours
Course Outline
Introduction to Vertex AI for Enterprise Applications
- Key requirements and challenges in enterprise AI
- Overview of Vertex AI enterprise capabilities
- Applications in highly regulated industries
Configuring Enterprise MLOps Pipelines
- Integrating Vertex AI with CI/CD workflows
- Strategies for automation and orchestration
- Practical exercise: constructing a deployment pipeline
Monitoring and Observability
- Real-time model monitoring and alerting systems
- Creating model performance dashboards
- Practical exercise: establishing monitoring workflows
Grounding and Gen AI Evaluation
- Anchoring models with enterprise data
- Using Gen AI evaluation libraries and tools
- Practical exercise: implementing evaluation workflows
Compliance and Governance in Vertex AI
- Features for data residency and access control
- Ensuring auditability and traceability
- Practical exercise: configuring compliance policies
Scaling and Enterprise Integration
- Scaling Vertex AI deployments
- Connecting with enterprise systems and APIs
- Practical exercise: enterprise-scale deployment
Case Studies and Best Practices
- Success stories from financial services, healthcare, and the public sector
- Insights from enterprise adoption experiences
- Best practices for sustainable long-term operations
Conclusion and Future Pathways
Requirements
- Practical experience in deploying ML models to production environments
- Working knowledge of CI/CD pipelines
- Foundational understanding of data governance and compliance frameworks
Target Audience
- MLOps engineers
- Platform teams
- Compliance leaders
Testimonials (1)
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