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Duration 14 hours
Course Outline
Introduction to Vertex AI in the Enterprise
- Key AI requirements and challenges in enterprise settings
- Overview of enterprise-specific Vertex AI features
- Applicability in regulated industries
Building Enterprise MLOps Pipelines
- Integrating Vertex AI into existing CI/CD workflows
- Implementing automation and orchestration
- Practical exercise: constructing a deployment pipeline
Monitoring and Observability
- Configuring live model monitoring and alert systems
- Creating model performance dashboards
- Practical exercise: establishing monitoring workflows
Grounding and Gen AI Evaluation
- Grounding models with enterprise-specific data
- Utilizing Gen AI evaluation libraries and tools
- Practical exercise: executing evaluation workflows
Compliance and Governance in Vertex AI
- Leveraging data residency and access control capabilities
- Ensuring auditability and traceability
- Practical exercise: setting up compliance policies
Scaling and Enterprise Integration
- Expanding Vertex AI deployments at scale
- Connecting with enterprise systems and APIs
- Practical exercise: managing enterprise-scale deployments
Case Studies and Best Practices
- Success stories from financial services, healthcare, and the public sector
- Insights gained from enterprise adoption journeys
- Best practices for sustained long-term operations
Summary and Future Pathways
Requirements
- Proven experience in deploying machine learning models to production environments
- Working knowledge of CI/CD pipelines
- Foundational understanding of data governance and compliance frameworks
Target Audience
- MLOps Engineers
- Platform Engineering Teams
- Compliance Officers and Leads
Testimonials (1)
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