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Duration 14 hours
Course Outline
Introduction to Vertex AI for the Enterprise
- Enterprise AI requirements and associated challenges
- Overview of Vertex AI enterprise capabilities
- Application use cases in regulated industries
Establishing Enterprise MLOps Pipelines
- Integrating Vertex AI with CI/CD workflows
- Strategies for automation and orchestration
- Hands-on lab: Constructing a deployment pipeline
Monitoring and Observability
- Implementing live model monitoring and alert systems
- Utilizing model performance dashboards
- Hands-on lab: Configuring monitoring workflows
Grounding and Gen AI Evaluation
- Grounding models with enterprise-specific data
- Exploring Gen AI evaluation libraries and tools
- Hands-on lab: Implementing evaluation workflows
Compliance and Governance in Vertex AI
- Data residency features and access control mechanisms
- Ensuring auditability and traceability
- Hands-on lab: Configuring compliance policies
Scaling and Enterprise Integration
- Scaling Vertex AI deployments effectively
- Integrating with broader enterprise systems and APIs
- Hands-on lab: Enterprise-scale deployment simulation
Case Studies and Best Practices
- Success stories from financial services, healthcare, and the public sector
- Key lessons from enterprise adoption experiences
- Best practices for sustaining long-term operations
Summary and Next Steps
Requirements
- Practical experience in deploying ML models to production environments.
- Knowledge of CI/CD pipeline architectures.
- Familiarity with data governance and compliance frameworks.
Target Audience
- MLOps engineers
- Platform teams
- Compliance leads
Testimonials (1)
easy steps in ML