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Duration 14 hours
Course Outline
Introduction to Vertex AI in Enterprise Contexts
- Enterprise AI needs and associated challenges
- Overview of Vertex AI enterprise features
- Applications in highly regulated sectors
Establishing Enterprise MLOps Pipelines
- Integrating Vertex AI with CI/CD workflows
- Process automation and orchestration
- Practical lab: constructing a deployment pipeline
Monitoring and Observability
- Real-time model monitoring and alerting systems
- Model performance dashboards
- Practical lab: configuring monitoring workflows
Grounding and Gen AI Assessment
- Anchoring models with enterprise data
- Gen AI evaluation libraries and instruments
- Practical lab: executing evaluation workflows
Compliance and Governance in Vertex AI
- Data residency and access management capabilities
- Ensuring auditability 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 Best Practices
- Success stories from financial services, healthcare, and the public sector
- Insights gained from enterprise adoption
- Best practices for sustained operations
Conclusion and Future Steps
Requirements
- Background in deploying ML models into production environments
- Knowledge of CI/CD pipelines
- Comprehension of data governance and compliance frameworks
Target Audience
- MLOps engineers
- Platform teams
- Compliance leads
Testimonials (1)
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