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Course Outline
Introduction to Enterprise Vertex AI
- Enterprise AI requirements and challenges
- Overview of Vertex AI enterprise features
- Use cases in regulated industries
Establishing Enterprise MLOps Pipelines
- Integrating Vertex AI with CI/CD workflows
- Automation and orchestration strategies
- Hands-on lab: Constructing a deployment pipeline
Monitoring and Observability
- Real-time model monitoring and alerting mechanisms
- Model performance dashboards
- Hands-on lab: Configuring monitoring workflows
Grounding and Gen AI Evaluation
- Grounding models with enterprise data
- Gen AI evaluation libraries and tools
- Hands-on lab: Implementing evaluation workflows
Compliance and Governance in Vertex AI
- Data residency and access control features
- Auditability and traceability
- Hands-on lab: Configuring compliance policies
Scaling and Enterprise Integration
- Scaling Vertex AI deployments
- Integration with 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
- Key lessons learned from enterprise adoption efforts
- Best practices for sustained operational excellence
Summary and Next Steps
Requirements
- Experience in deploying ML models in production environments
- Proficiency with CI/CD pipelines
- Understanding of data governance and compliance frameworks
Target Audience
- MLOps Engineers
- Platform Engineering Teams
- Compliance Leads
14 Hours
Testimonials (1)
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