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Duration 14 hours
Course Outline
Introduction to Vertex AI for Enterprise Environments
- Enterprise AI needs and associated challenges
- Overview of Vertex AI's enterprise capabilities
- Application in regulated industries
Establishing Enterprise MLOps Pipelines
- Connecting Vertex AI with CI/CD workflows
- Process automation and orchestration
- Practical lab: Constructing a deployment pipeline
Monitoring and Observability
- Real-time model monitoring and alert mechanisms
- Performance analytics and dashboards
- Practical lab: Configuring monitoring workflows
Grounding and Generative AI Evaluation
- Grounding models using enterprise datasets
- Generative AI evaluation frameworks and libraries
- Practical lab: Implementing evaluation workflows
Compliance and Governance in Vertex AI
- Data residency and access control mechanisms
- Audit trails and traceability
- Practical lab: Setting up compliance policies
Scaling and Enterprise Integration
- Scaling Vertex AI deployments
- Integration with enterprise systems and APIs
- Practical lab: Enterprise-scale deployment
Case Studies and Industry Best Practices
- Success stories from financial services, healthcare, and the public sector
- Key lessons from enterprise adoption
- Best practices for sustained operations
Summary and Next Steps
Requirements
- Practical experience in deploying ML models to production environments
- Proficiency with CI/CD pipelines
- A solid grasp of data governance and compliance frameworks
Target Audience
- MLOps Engineers
- Platform Teams
- Compliance Officers
Testimonials (1)
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