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Course Outline
Vertex AI for Enterprise: An Introduction
- Key enterprise AI requirements and associated challenges
- Overview of Vertex AI’s enterprise-oriented features
- Applications in heavily regulated sectors
Building Enterprise MLOps Pipelines
- Integrating Vertex AI into CI/CD workflows
- Strategies for automation and orchestration
- Practical session: Constructing a deployment pipeline
Monitoring and Observability
- Real-time model monitoring and alerting mechanisms
- Utilizing model performance dashboards
- Practical session: Establishing monitoring workflows
Grounding and Gen AI Evaluation
- Anchoring models with enterprise-specific data
- Exploring Gen AI evaluation libraries and utilities
- Practical session: Executing evaluation workflows
Compliance and Governance in Vertex AI
- Managing data residency and access control features
- Ensuring auditability and traceability
- Practical session: Configuring compliance policies
Scaling and Enterprise Integration
- Methods for scaling Vertex AI deployments
- Connecting with enterprise systems and APIs
- Practical session: Enterprise-scale deployment scenarios
Case Studies and Industry Best Practices
- Success narratives in finance, healthcare, and public services
- Key takeaways from enterprise adoption experiences
- Best practices for sustained long-term operations
Conclusion and Future Directions
Requirements
- Practical experience in deploying ML models to production environments
- Knowledge of CI/CD pipeline mechanisms
- Conceptual grasp of data governance and compliance frameworks
Target Audience
- MLOps engineers
- Platform engineering teams
- Compliance officers
14 Hours
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