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Duration 14 hours
Course Outline
Introduction to Vertex AI in Enterprise Settings
- Key enterprise AI requirements and associated challenges
- Overview of Vertex AI’s enterprise features
- Application use cases within regulated industries
Establishing Enterprise MLOps Pipelines
- Integrating Vertex AI with CI/CD workflows
- Automation and orchestration techniques
- Practical lab: constructing a deployment pipeline
Monitoring and Observability Practices
- Live model monitoring and alerting mechanisms
- Creating model performance dashboards
- Practical lab: setting up monitoring workflows
Grounding and Evaluating Generative AI
- Grounding models using enterprise data
- Utilizing Generative AI evaluation libraries and tools
- Practical lab: implementing evaluation workflows
Compliance and Governance within Vertex AI
- Data residency features and access control mechanisms
- Ensuring auditability and traceability
- Practical lab: configuring compliance policies
Scaling and Enterprise Integration
- Scaling Vertex AI deployments
- Integration with broader enterprise systems and APIs
- Practical lab: executing enterprise-scale deployments
Case Studies and Industry Best Practices
- Success stories from the financial services, healthcare, and public sectors
- Insights gained from enterprise adoption experiences
- Best practices for sustaining long-term operations
Summary and Recommended Next Steps
Requirements
- Practical experience in deploying ML models to production environments
- Familiarity with CI/CD pipeline workflows
- Understanding of data governance and compliance frameworks
Target Audience
- MLOps engineers
- Platform teams
- Compliance leads
Testimonials (1)
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