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Course Outline
Introduction to Enterprise Vertex AI
- Specific AI requirements and challenges in enterprise contexts
- Overview of Vertex AI enterprise capabilities
- Application in highly regulated industries
Configuring Enterprise MLOps Pipelines
- Integration of 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
- Model performance dashboards
- Practical lab: implementing monitoring workflows
Grounding and Gen AI Evaluation
- Grounding models using enterprise data
- Gen AI evaluation libraries and tooling
- Practical lab: implementing evaluation workflows
Compliance and Governance in Vertex AI
- Data residency and access control features
- Auditability and traceability mechanisms
- Practical lab: configuring 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 gained from enterprise adoption
- Best practices for sustained operations
Summary and Next Steps
Requirements
- Practical experience deploying ML models in production environments
- Proficiency with CI/CD pipelines
- Knowledge of data governance and compliance frameworks
Intended Audience
- MLOps engineers
- Platform teams
- Compliance leads
14 Hours
Testimonials (1)
easy steps in ML
