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Duration 14 hours
Course Outline
Introduction to Vertex AI for the Enterprise
- Enterprise AI requirements and associated challenges
- Overview of Vertex AI enterprise capabilities
- Application of use cases in regulated industries
Establishing Enterprise MLOps Pipelines
- Integration of Vertex AI with CI/CD workflows
- Automation and orchestration strategies
- Practical lab: Constructing a deployment pipeline
Monitoring and Observability
- Live model monitoring and alerting mechanisms
- Model performance dashboards and metrics
- Practical lab: Configuring monitoring workflows
Grounding and Gen AI Evaluation
- Grounding models with enterprise-specific data
- Utilising Gen AI evaluation libraries and tools
- Practical lab: Implementing evaluation workflows
Compliance and Governance in Vertex AI
- Data residency and access control features
- Auditability and traceability considerations
- Practical lab: Configuring compliance policies
Scaling and Enterprise Integration
- Strategies for scaling Vertex AI deployments
- Integration with enterprise systems and APIs
- Practical lab: Enterprise-scale deployment scenarios
Case Studies and Best Practices
- Success stories from financial services, healthcare, and the public sector
- Lessons learned from enterprise adoption experiences
- Best practices for sustained long-term operations
Summary and Next Steps
Requirements
- Practical experience in deploying ML models to production environments
- Familiarity with CI/CD pipelines
- Working knowledge of data governance and compliance frameworks
Target Audience
- MLOps engineers
- Platform teams
- Compliance leads
Testimonials (1)
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