Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Introduction to Vertex AI in Enterprise Contexts
- Core AI requirements and challenges in enterprise settings
- Overview of Vertex AI’s enterprise-specific features
- Application use cases within regulated industries
Configuring Enterprise MLOps Pipelines
- Integrating Vertex AI with CI/CD workflows
- Strategies for automation and orchestration
- Practical lab: constructing a deployment pipeline
Monitoring and Observability Frameworks
- Implementing live model monitoring and alerting systems
- Utilizing model performance dashboards
- Practical lab: configuring monitoring workflows
Grounding and Gen AI Evaluation Strategies
- Anchoring models with enterprise-specific data
- Exploring Gen AI evaluation libraries and tooling
- Practical lab: executing evaluation workflows
Compliance and Governance in Vertex AI
- Managing data residency and access control mechanisms
- Ensuring auditability and traceability
- Practical lab: setting up compliance policies
Scaling and Enterprise Integration
- Strategies for scaling Vertex AI deployments
- Integrating with broader enterprise systems and APIs
- Practical lab: executing enterprise-scale deployments
Case Studies and Industry Best Practices
- Success stories from financial services, healthcare, and the public sector
- Key lessons from enterprise adoption initiatives
- Best practices for sustained long-term operations
Summary and Recommended Next Steps
Requirements
- Practical experience in deploying ML models to production environments
- Proficiency with CI/CD pipeline workflows
- Solid understanding of data governance and compliance frameworks
Target Audience
- MLOps Engineers
- Platform Engineering Teams
- Compliance and Governance Leads
14 Hours
Testimonials (1)
easy steps in ML