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Course Outline
Introduction to Vertex AI for Enterprise Use
- Enterprise AI requirements and associated challenges
- Overview of Vertex AI enterprise capabilities
- Use cases within regulated industries
Building Enterprise MLOps Pipelines
- Integrating Vertex AI with CI/CD workflows
- Automation and orchestration techniques
- Hands-on lab: constructing a deployment pipeline
Monitoring and Observability
- Real-time model monitoring and alert systems
- Model performance dashboards
- Hands-on lab: configuring monitoring workflows
Grounding and Gen AI Evaluation
- Grounding models using 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 capabilities
- Auditability and traceability measures
- Hands-on lab: setting up compliance policies
Scaling and Enterprise Integration
- Scaling Vertex AI deployments
- Integration with enterprise systems and APIs
- Hands-on lab: enterprise-scale deployment
Case Studies and Best Practices
- Success stories from financial services, healthcare, and the public sector
- Key takeaways from enterprise adoption
- Best practices for sustained operations
Summary and Next Steps
Requirements
- Experience deploying ML models in production environments
- Knowledge of CI/CD pipelines
- Understanding of data governance and compliance frameworks
Target Audience
- MLOps engineers
- Platform teams
- Compliance leads
14 Hours
Testimonials (1)
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