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Duration 14 hours
Course Outline
Introduction to Vertex AI for the Enterprise
- Key enterprise AI requirements and associated challenges
- Overview of Vertex AI's enterprise-specific features
- Relevant use cases within regulated industries
Configuring Enterprise MLOps Pipelines
- Seamlessly integrating Vertex AI with existing CI/CD workflows
- Strategies for automation and orchestration
- Practical lab: Constructing a robust deployment pipeline
Monitoring and Observability
- Implementing live model monitoring and alerting systems
- Developing comprehensive model performance dashboards
- Practical lab: Establishing effective monitoring workflows
Grounding and Gen AI Evaluation
- Anchoring models using enterprise data for grounding
- Utilizing specialized Gen AI evaluation libraries and tools
- Practical lab: Deploying evaluation workflows in practice
Compliance and Governance in Vertex AI
- Leveraging data residency and access control functionalities
- Ensuring auditability and full traceability
- Practical lab: Configuring and enforcing compliance policies
Scaling and Enterprise Integration
- Techniques for scaling Vertex AI deployments
- Integrating with broader enterprise systems and APIs
- Practical lab: Executing an enterprise-scale deployment
Case Studies and Best Practices
- Examining success stories from financial services, healthcare, and the public sector
- Key lessons derived from enterprise adoption experiences
- Best practices for sustaining long-term operations
Summary and Next Steps
Requirements
- Proven experience in deploying machine learning models to production environments
- Working knowledge of CI/CD pipeline architecture
- Sound understanding of data governance standards and compliance frameworks
Target Audience
- MLOps engineers
- Platform engineering teams
- Compliance and governance leads
Testimonials (1)
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