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Duration 14 hours
Course Outline
Introduction to Vertex AI in Enterprise Settings
- Key AI requirements and challenges faced by enterprises
- Overview of enterprise-centric features in Vertex AI
- Application scenarios in heavily regulated industries
Establishing Enterprise MLOps Pipelines
- Seamless integration of Vertex AI with CI/CD workflows
- Strategies for automation and process orchestration
- Practical exercise: Developing a robust deployment pipeline
Monitoring and Observability Practices
- Implementing live model monitoring and automated alerting
- Creating actionable model performance dashboards
- Practical exercise: Configuring comprehensive monitoring workflows
Grounding and Evaluation of Generative AI
- Applying grounding techniques using proprietary enterprise data
- Leveraging specialized Gen AI evaluation libraries and tools
- Practical exercise: Executing structured evaluation workflows
Compliance and Governance with Vertex AI
- Configuring data residency and granular access control features
- Ensuring full auditability and process traceability
- Practical exercise: Defining and applying compliance policies
Scaling and Enterprise System Integration
- Strategies for scaling Vertex AI deployments effectively
- Integrating Vertex AI with existing enterprise systems and APIs
- Practical exercise: Managing enterprise-scale deployments
Case Studies and Industry Best Practices
- Success stories from financial services, healthcare, and the public sector
- Key takeaways from enterprise adoption experiences
- Best practices for sustaining long-term operations
Summary and Path Forward
Requirements
- Proven experience in deploying ML models in production environments
- Proficiency with CI/CD pipeline mechanisms
- Solid understanding of data governance standards and compliance frameworks
Target Audience
- MLOps engineers
- Platform engineering teams
- Compliance and governance leads
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