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Duration 14 hours
Course Outline
Introduction to Vertex AI in an Enterprise Context
- Enterprise AI requirements and associated challenges
- Overview of Vertex AI’s enterprise capabilities
- Use cases within regulated industries
Establishing Enterprise MLOps Pipelines
- Integrating Vertex AI with CI/CD workflows
- Automation and orchestration techniques
- Hands-on lab: Constructing a deployment pipeline
Monitoring and Observability
- Live model monitoring and alerting mechanisms
- Model performance dashboards
- Hands-on lab: Configuring monitoring workflows
Grounding and Gen AI Evaluation
- Grounding models using enterprise data
- Gen AI evaluation libraries and tools
- Hands-on lab: Implementing evaluation workflows
Compliance and Governance within Vertex AI
- Data residency and access control features
- Auditability and traceability
- Hands-on lab: Setting up 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 from financial services, healthcare, and the public sector
- Key lessons from enterprise adoption
- Best practices for long-term operations
Summary and Next Steps
Requirements
- Proven experience deploying ML models in production environments.
- Proficiency with CI/CD pipelines.
- A solid grasp of data governance and compliance frameworks.
Target Audience
- MLOps engineers.
- Platform teams.
- Compliance leads.
Testimonials (1)
easy steps in ML