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Duration 14 hours
Course Outline
Introduction to Enterprise Vertex AI
- Specific AI requirements and challenges in enterprise settings.
- Overview of enterprise-specific features in Vertex AI.
- Application use cases within highly regulated industries.
Configuring Enterprise MLOps Pipelines
- Integrating Vertex AI with existing CI/CD workflows.
- Strategies for automation and orchestration.
- Practical exercise: constructing a model deployment pipeline.
Monitoring and Observability Practices
- Implementing live model monitoring and alerting mechanisms.
- Utilizing model performance dashboards.
- Practical exercise: establishing monitoring workflows.
Grounding and Evaluating Generative AI
- Applying grounding techniques using enterprise datasets.
- Review of evaluation libraries and tools for generative AI.
- Practical exercise: implementing evaluation workflows.
Compliance and Governance in Vertex AI
- Features for data residency and access control.
- Ensuring auditability and traceability.
- Practical exercise: setting up compliance policies.
Scaling and Enterprise System Integration
- Techniques for scaling Vertex AI deployments.
- Connecting with enterprise systems and APIs.
- Practical exercise: deploying at enterprise scale.
Case Studies and Industry Best Practices
- Success stories from financial services, healthcare, and the public sector.
- Key lessons from enterprise adoption journeys.
- Best practices for sustainable long-term operations.
Conclusions and Future Pathways
Requirements
- Practical experience in deploying machine learning models to production environments.
- Working knowledge of CI/CD pipeline mechanisms.
- Comprehensive understanding of data governance standards and compliance frameworks.
Target Audience
- MLOps Engineers
- Platform Engineering Teams
- Compliance and Governance Officers
Testimonials (1)
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