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Duration 14 hours
Course Outline
Introduction to Vertex AI in an Enterprise Context
- Key AI requirements and challenges faced by enterprises
- Overview of Vertex AI’s enterprise-specific features
- Application scenarios in highly regulated sectors
Establishing Enterprise MLOps Pipelines
- Connecting Vertex AI with CI/CD workflows
- Techniques for automation and orchestration
- Practical exercise: Constructing a deployment pipeline
Monitoring and Observability Practices
- Implementing live model monitoring and alert systems
- Creating model performance dashboards
- Practical exercise: Configuring monitoring workflows
Grounding and Gen AI Assessment
- Grounding models using enterprise datasets
- Exploration of Gen AI evaluation libraries and utilities
- Practical exercise: Executing evaluation workflows
Compliance and Governance with Vertex AI
- Features for data residency and access management
- Ensuring auditability and traceability
- Practical exercise: Setting up compliance policies
Scaling and Enterprise-System Integration
- Strategies for scaling Vertex AI deployments
- Integration with internal enterprise systems and APIs
- Practical exercise: Deploying at enterprise scale
Case Studies and Industry Best Practices
- Success stories from financial services, healthcare, and the public sector
- Insights gained from enterprise adoption experiences
- Recommendations for sustainable long-term operations
Conclusion and Future Recommendations
Requirements
- Practical experience in deploying ML models to production environments
- Knowledge of CI/CD pipeline mechanics
- Familiarity with data governance and compliance frameworks
Target Audience
- MLOps engineers
- Platform engineering teams
- Compliance officers
Testimonials (1)
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