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Duration 14 hours
Course Outline
Introduction to Vertex AI in an Enterprise Context
- Key enterprise AI requirements and associated challenges
- Overview of Vertex AI’s enterprise-focused features
- Application scenarios in highly regulated industries
Building Enterprise-Grade MLOps Pipelines
- Seamless integration of Vertex AI with CI/CD workflows
- Strategies for automation and orchestration
- Hands-on lab: Constructing a robust deployment pipeline
Monitoring and Observability
- Implementing live model monitoring and alerting mechanisms
- Utilizing model performance dashboards for visibility
- Hands-on lab: Establishing comprehensive monitoring workflows
Grounding and Gen AI Evaluation
- Grounding models using proprietary enterprise data
- Leveraging Gen AI evaluation libraries and specialized tools
- Hands-on lab: Implementing structured evaluation workflows
Compliance and Governance in Vertex AI
- Configuring data residency and fine-grained access control features
- Ensuring auditability and traceability of actions
- Hands-on lab: Configuring and managing compliance policies
Scaling and Enterprise Integration
- Scalability strategies for Vertex AI deployments
- Integration with existing enterprise systems and APIs
- Hands-on lab: Executing an enterprise-scale deployment
Case Studies and Industry Best Practices
- Success stories from financial services, healthcare, and the public sector
- Key takeaways from enterprise adoption experiences
- Best practices for sustaining long-term operations
Summary and Recommended Next Steps
Requirements
- Proven experience in deploying ML models to production environments
- Solid working knowledge of CI/CD pipelines
- A clear understanding of data governance principles and compliance frameworks
Target Audience
- MLOps engineers
- Platform engineering teams
- Compliance leads and officers
Testimonials (1)
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