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Duration 14 hours
Course Outline
Introduction to Vertex AI for Enterprise Use
- Addressing enterprise AI requirements and associated challenges
- Overview of Vertex AI’s enterprise capabilities
- Application use cases in highly regulated sectors
Establishing Enterprise MLOps Pipelines
- Integrating Vertex AI with existing CI/CD workflows
- Implementing automation and orchestration
- Hands-on lab: constructing a deployment pipeline
Monitoring and Observability
- Configuring live model monitoring and alerting systems
- Creating model performance dashboards
- Hands-on lab: setting up monitoring workflows
Grounding and Gen AI Evaluation
- Grounding models using enterprise data
- Utilizing Gen AI evaluation libraries and tools
- Hands-on lab: implementing evaluation workflows
Compliance and Governance within Vertex AI
- Managing data residency and access control features
- Ensuring auditability and traceability
- Hands-on lab: configuring compliance policies
Scaling and Enterprise Integration
- Scaling Vertex AI deployments for high-volume environments
- Integrating with enterprise systems and APIs
- Hands-on lab: executing an enterprise-scale deployment
Case Studies and Best Practices
- Examining success stories from financial services, healthcare, and the public sector
- Key takeaways from enterprise adoption processes
- Best practices for sustaining long-term operations
Summary and Recommended Next Steps
Requirements
- Experience in deploying ML models to production environments
- Familiarity with CI/CD pipelines
- Knowledge of data governance and compliance frameworks
Target Audience
- MLOps engineers
- Platform teams
- Compliance leads
Testimonials (1)
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