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Duration 14 hours
Course Outline
Overview of Vertex AI for Enterprise Applications
- Addressing enterprise AI operational needs and challenges
- Summary of Vertex AI capabilities for organizational use
- Applications in highly regulated government sectors
Establishing Enterprise MLOps Workflows
- Embedding Vertex AI within continuous integration and delivery processes
- Workflow automation and orchestration strategies
- Practical exercise: Constructing a deployment pipeline
Surveillance and Observability Capabilities
- Real-time model monitoring and notification systems
- Visualizing model performance metrics
- Practical exercise: Configuring monitoring protocols
Model Grounding and Generative AI Assessment
- Anchoring models with institutional data resources
- Utilizing libraries and frameworks for generative AI evaluation
- Practical exercise: Executing assessment workflows
Compliance and Governance within Vertex AI
- Features for data sovereignty and access management
- Ensuring audit trails and operational traceability
- Practical exercise: Establishing compliance directives
Scalability and System Integration
- Expanding Vertex AI implementations at scale
- Connecting with legacy enterprise infrastructure and APIs
- Practical exercise: Implementing large-scale deployments
Empirical Evidence and Operational Standards
- Documented outcomes in finance, healthcare, and public administration
- Insights from large-scale enterprise adoption
- Recommended protocols for sustained operational success
Conclusions and Future Directions
Requirements
- Proven experience in deploying machine learning models to production environments
- Working knowledge of continuous integration and continuous delivery (CI/CD) pipelines
- Comprehension of data stewardship and regulatory compliance frameworks
Intended Participants
- MLOps engineers
- Platform engineering teams
- Compliance officers and governance leads
Testimonials (1)
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