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Duration 14 hours
Course Outline
Introduction to Enterprise-Grade Vertex AI
- Key requirements and challenges for enterprise AI
- Overview of Vertex AI's enterprise-specific features
- Application scenarios in highly regulated industries
Establishing Enterprise MLOps Pipelines
- Integrating Vertex AI into CI/CD workflows
- Strategies for automation and orchestration
- Practical exercise: Creating a deployment pipeline
Monitoring and Observability
- Implementing live model monitoring and alert systems
- Designing model performance dashboards
- Practical exercise: Configuring monitoring workflows
Grounding and Evaluating Gen AI
- Grounding models using proprietary enterprise data
- Overview of Gen AI evaluation libraries and tools
- Practical exercise: Implementing evaluation workflows
Compliance and Governance within Vertex AI
- Features for data residency and access control
- Ensuring auditability and traceability
- Practical exercise: Setting up compliance policies
Scaling and Enterprise Integration
- Scaling Vertex AI deployments effectively
- Integrating with broader 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
- Insights gained from enterprise adoption experiences
- Best practices for sustaining long-term operations
Summary and Future Directions
Requirements
- Proven experience in deploying ML models into production environments
- Working knowledge of CI/CD pipelines
- Conceptual understanding of data governance and compliance frameworks
Target Audience
- MLOps engineers
- Platform engineering teams
- Compliance leaders
Testimonials (1)
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