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.
Duration 14 hours
Course Outline
Introduction to Vertex AI for Enterprise Environments
- Key AI requirements and challenges in enterprise settings
- Overview of Vertex AI enterprise-specific features
- Application examples in highly regulated industries
Establishing Enterprise MLOps Pipelines
- Integrating Vertex AI into existing CI/CD workflows
- Strategies for automation and orchestration
- Practical lab: constructing an end-to-end deployment pipeline
Monitoring and Observability Practices
- Implementing live model monitoring and alerting systems
- Creating model performance dashboards
- Practical lab: configuring monitoring workflows
Grounding and Gen AI Assessment
- Applying grounding techniques using enterprise data
- Utilizing Gen AI evaluation libraries and associated tools
- Practical lab: executing evaluation workflows
Compliance and Governance within Vertex AI
- Managing data residency and access control mechanisms
- Ensuring auditability and full traceability
- Practical lab: setting up compliance policies
Scaling and Enterprise System Integration
- Strategies for scaling Vertex AI deployments
- Integrating with enterprise systems and external APIs
- Practical lab: deploying at enterprise scale
Case Studies and Industry Best Practices
- Success stories from financial services, healthcare, and the public sector
- Key lessons from enterprise adoption journeys
- Best practices for sustainable long-term operations
Course Summary and Future Directions
Requirements
- Practical experience in deploying ML models to production environments
- Proficiency with CI/CD pipeline architectures
- A solid grasp of data governance and compliance frameworks
Target Audience
- MLOps engineers
- Platform engineering teams
- Compliance and governance leads
Testimonials (1)
easy steps in ML
