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Duration 14 hours
Course Outline
Overview of Vertex AI for Enterprise Use
- Specific AI requirements and operational challenges in enterprise settings
- Key features of Vertex AI for corporate applications
- Relevant use cases in highly regulated industries
Configuring Enterprise MLOps Pipelines
- Connecting Vertex AI with CI/CD workflows
- Strategies for automation and task orchestration
- Practical exercise: constructing a deployment pipeline
Monitoring and Observability Capabilities
- Real-time model monitoring and alert configuration
- Visualization of model performance through dashboards
- Practical exercise: setting up monitoring workflows
Grounding and Evaluation of Generative AI
- Applying grounding to models using enterprise data
- Available libraries and tools for Generative AI assessment
- Practical exercise: implementing evaluation workflows
Compliance and Governance in Vertex AI
- Features for data residency and access management
- Ensuring auditability and full traceability
- Practical exercise: configuring compliance policies
Scaling and Enterprise Integration
- Scaling strategies for Vertex AI deployments
- Integration with broader enterprise systems and APIs
- Practical exercise: enterprise-scale deployment
Case Studies and Industry Best Practices
- Success stories from financial services, healthcare, and the public sector
- Key insights from enterprise adoption experiences
- Best practices for sustaining long-term operations
Conclusion and Recommended Next Steps
Requirements
- Prior experience in deploying ML models to production environments
- Proficiency with CI/CD pipeline architectures
- Working knowledge of data governance and compliance standards
Target Audience
- MLOps engineers
- Platform engineering teams
- Compliance officers
Testimonials (1)
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