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Duration 14 hours
Course Outline
Introduction to Vertex AI for the Enterprise
- Enterprise AI requirements and associated challenges
- Overview of Vertex AI enterprise features
- Use cases within regulated industries
Configuring Enterprise MLOps Pipelines
- Integrating Vertex AI into CI/CD workflows
- Automation and orchestration strategies
- Practical lab: constructing a deployment pipeline
Monitoring and Observability
- Live model monitoring and alerting mechanisms
- Model performance dashboards
- Practical lab: establishing monitoring workflows
Grounding and Gen AI Evaluation
- Grounding models using enterprise data
- Gen AI evaluation libraries and toolkits
- Practical lab: implementing evaluation workflows
Compliance and Governance in Vertex AI
- Data residency and access control capabilities
- Auditability and traceability features
- Practical lab: setting up compliance policies
Scaling and Enterprise Integration
- Scaling Vertex AI deployments
- Integration with enterprise systems and APIs
- Practical lab: enterprise-scale deployment
Case Studies and Best Practices
- Success stories from financial services, healthcare, and the public sector
- Key lessons from enterprise adoption
- Best practices for sustained operations
Summary and Next Steps
Requirements
- Experience deploying ML models in production environments
- Familiarity with CI/CD pipelines
- Understanding of data governance and compliance frameworks
Target Audience
- MLOps engineers
- Platform teams
- Compliance leads
Testimonials (1)
easy steps in ML