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Course Outline
Introduction to Vertex AI for the Enterprise
- Key requirements and challenges in enterprise AI
- Overview of enterprise-specific Vertex AI features
- Application use cases in regulated industries
Constructing Enterprise MLOps Pipelines
- Seamless integration of Vertex AI with CI/CD workflows
- Strategies for automation and orchestration
- Practical lab: building a robust deployment pipeline
Monitoring and Observability
- Implementing live model monitoring and automated alerting
- Designing effective model performance dashboards
- Practical lab: configuring comprehensive monitoring workflows
Grounding and Generative AI Evaluation
- Grounding models using proprietary enterprise data
- Utilizing evaluation libraries and tools for Generative AI
- Practical lab: implementing structured evaluation workflows
Compliance and Governance in Vertex AI
- Leveraging data residency and access control features
- Ensuring auditability and full traceability
- Practical lab: configuring and applying compliance policies
Scaling and Enterprise Integration
- Scaling Vertex AI deployments for high-demand environments
- Integrating with existing enterprise systems and APIs
- Practical lab: managing enterprise-scale deployments
Case Studies and Best Practices
- Success stories from financial services, healthcare, and the public sector
- Key lessons learned from enterprise adoption initiatives
- Best practices for sustaining long-term operations
Summary and Future Directions
Requirements
- Practical experience in deploying machine learning models to production environments
- Working knowledge of CI/CD pipelines
- A solid grasp of data governance principles and compliance frameworks
Target Audience
- MLOps Engineers
- Platform Engineering Teams
- Compliance Officers and Leads
14 Hours
Testimonials (1)
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