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Duration 14 hours
Course Outline
Introduction to Enterprise Vertex AI
- AI requirements and challenges specific to enterprise environments
- Overview of Vertex AI's enterprise features
- Applications in highly regulated industries
Establishing Enterprise MLOps Pipelines
- Integration of Vertex AI with CI/CD workflows
- Automation and orchestration techniques
- Practical lab: constructing a deployment pipeline
Monitoring and Observability
- Real-time model monitoring and alert systems
- Performance dashboards for model tracking
- Practical lab: configuring monitoring workflows
Grounding and Generative AI Evaluation
- Grounding models using enterprise data sources
- Evaluation libraries and tools for Generative AI
- Practical lab: executing evaluation workflows
Compliance and Governance in Vertex AI
- Features for data residency and access control
- Ensuring auditability and traceability
- Practical lab: setting up compliance policies
Scaling and Enterprise Integration
- Scaling Vertex AI deployments for high volume
- Integration with corporate systems and APIs
- Practical lab: enterprise-scale deployment
Case Studies and Best Practices
- Success stories from financial services, healthcare, and the public sector
- Insights gained from enterprise adoption experiences
- Best practices for sustained long-term operations
Summary and Next Steps
Requirements
- Practical experience in deploying machine learning models to production environments
- Knowledge of CI/CD pipeline methodologies
- Understanding of data governance principles and compliance frameworks
Target Audience
- MLOps engineers
- Platform engineering teams
- Compliance officers
Testimonials (1)
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