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Course Outline
Introduction to Vertex AI for the Enterprise
- Enterprise AI requirements and challenges.
- Overview of Vertex AI enterprise features.
- Use cases in regulated industries.
Setting Up Enterprise MLOps Pipelines
- Integrating Vertex AI with CI/CD workflows.
- Automation and orchestration.
- Hands-on lab: building a deployment pipeline.
Monitoring and Observability
- Live model monitoring and alerting.
- Model performance dashboards.
- Hands-on lab: setting up monitoring workflows.
Grounding and Gen AI Evaluation
- Grounding models with enterprise data.
- Gen AI evaluation libraries and tools.
- Hands-on lab: implementing evaluation workflows.
Compliance and Governance in Vertex AI
- Data residency and access control features.
- Auditability and traceability.
- Hands-on lab: configuring 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 in financial services, healthcare, and public sector.
- Lessons learned in enterprise adoption.
- Best practices for long-term operations.
Summary and Next Steps
Requirements
- Experience deploying ML models in production.
- Familiarity with CI/CD pipelines.
- Understanding of data governance and compliance frameworks.
Audience
- MLOps engineers.
- Platform teams.
- Compliance leads.
14 Hours
Testimonials (1)
easy steps in ML
