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Course Outline
Introduction to Vertex AI for the Enterprise
- Enterprise AI requirements and key challenges
- Overview of Vertex AI enterprise capabilities
- Application in regulated industries
Configuring Enterprise MLOps Pipelines
- Integrating Vertex AI with CI/CD workflows
- Automation and orchestration strategies
- Practical lab: constructing a deployment pipeline
Monitoring and Observability
- Real-time model monitoring and alerting systems
- Performance dashboards for models
- Practical lab: establishing monitoring workflows
Grounding and Generative AI Evaluation
- Anchoring models with enterprise data
- Generative AI evaluation libraries and tools
- Practical lab: executing evaluation workflows
Compliance and Governance in Vertex AI
- Data residency and access control mechanisms
- Auditability and traceability features
- Practical lab: setting up compliance policies
Scaling and Enterprise Integration
- Scaling Vertex AI deployments
- Integration with enterprise systems and APIs
- Practical lab: 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 long-term operations
Summary and Next Steps
Requirements
- Experience in deploying ML models to 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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