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Duration 14 hours
Course Outline
Introduction to Vertex AI in Enterprise Contexts
- Key enterprise AI requirements and associated challenges
- Overview of Vertex AI’s enterprise-centric features
- Application scenarios in highly regulated industries
Establishing Enterprise MLOps Pipelines
- Seamless integration of Vertex AI with CI/CD workflows
- Strategies for automation and orchestration
- Practical lab: Constructing a deployment pipeline
Monitoring and Observability Practices
- Implementing live model monitoring and alert mechanisms
- Utilizing model performance dashboards
- Practical lab: Configuring monitoring workflows
Grounding Techniques and Gen AI Evaluation
- Anchoring models with enterprise-specific data
- Application of Gen AI evaluation libraries and tools
- Practical lab: Executing evaluation workflows
Compliance and Governance within Vertex AI
- Managing data residency and access control features
- Ensuring auditability and traceability
- Practical lab: Setting up compliance policies
Scaling and Enterprise Integration
- Methods for scaling Vertex AI deployments
- Connecting with enterprise systems and APIs
- Practical lab: Enterprise-scale deployment implementation
Case Studies and Industry Best Practices
- Success narratives from financial services, healthcare, and the public sector
- Key lessons from enterprise adoption processes
- Best practices for sustaining long-term operations
Conclusions and Recommended Next Steps
Requirements
- Proven experience in deploying ML models to production environments
- Working knowledge of CI/CD pipelines
- Conceptual understanding of data governance and compliance frameworks
Target Audience
- MLOps engineers
- Platform engineering teams
- Compliance leads
Testimonials (1)
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