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Duration 14 hours
Course Outline
Introduction to Vertex AI for the Enterprise
- Enterprise AI requirements and key challenges
- Overview of Vertex AI enterprise features
- Applications in regulated industries
Establishing Enterprise MLOps Pipelines
- Integrating Vertex AI with CI/CD workflows
- Automation and orchestration techniques
- Hands-on lab: constructing a deployment pipeline
Monitoring and Observability
- Live model monitoring and alerting mechanisms
- Model performance dashboards
- Hands-on lab: configuring monitoring workflows
Grounding and Gen AI Evaluation
- Grounding models using enterprise data
- Gen AI evaluation libraries and tooling
- Hands-on lab: implementing evaluation workflows
Compliance and Governance in Vertex AI
- Data residency and access control capabilities
- Auditability and traceability measures
- Hands-on lab: setting up compliance policies
Scaling and Enterprise Integration
- Scaling Vertex AI deployments
- Integration with enterprise systems and APIs
- Hands-on 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
Audience
- MLOps engineers
- Platform teams
- Compliance leads
Testimonials (1)
easy steps in ML