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Course Outline
Introductory Overview: Vertex AI for Enterprise Use
- Key AI requirements and challenges in enterprise settings.
- High-level review of Vertex AI's enterprise-specific features.
- Application of AI in highly regulated industries.
Configuring Enterprise MLOps Pipelines
- Seamlessly integrating Vertex AI into CI/CD workflows.
- Strategies for automation and task orchestration.
- Practical session: constructing an end-to-end deployment pipeline.
Monitoring and Observability Practices
- Implementing live model monitoring and automated alerting.
- Designing and interpreting model performance dashboards.
- Practical session: establishing comprehensive monitoring workflows.
Grounding Strategies and Gen AI Evaluation
- Anchorinng models using proprietary enterprise data.
- Leveraging dedicated evaluation libraries and tools for Gen AI.
- Practical session: executing and validating evaluation workflows.
Compliance and Governance within Vertex AI
- Managing data residency and access control mechanisms.
- Ensuring auditability and full traceability of actions.
- Practical session: setting up and verifying compliance policies.
Scaling for Enterprise Integration
- Techniques for scaling Vertex AI deployments effectively.
- Connecting with broader enterprise systems and APIs.
- Practical session: managing an enterprise-scale deployment scenario.
Case Studies and Industry Best Practices
- Success stories from financial services, healthcare, and the public sector.
- Key lessons learned from enterprise-wide AI adoption.
- Recommendations for sustainable long-term operations.
Recap and Future Directions
Requirements
- Proven experience in deploying ML models to production environments.
- Proficiency with CI/CD pipeline workflows.
- A solid grasp of data governance principles and compliance frameworks.
Intended Audience
- MLOps Engineers
- Platform Engineering Teams
- Compliance Officers and Leads
14 Hours
Testimonials (1)
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