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Duration 14 hours
Course Outline
Intro to Vertex AI in Enterprise Settings
- Key AI demands and challenges in corporate environments
- Overview of Vertex AI's enterprise capabilities
- Applications in highly regulated sectors
Building Enterprise MLOps Pipelines
- Linking Vertex AI with CI/CD processes
- Workflow automation and orchestration
- Practical exercise: creating a deployment pipeline
Monitoring and Observability
- Real-time model tracking and alert mechanisms
- Performance visualization dashboards
- Practical exercise: configuring monitoring workflows
Grounding and Gen AI Assessment
- Anchoring models using enterprise datasets
- Libraries and tools for Gen AI evaluation
- Practical exercise: executing assessment workflows
Compliance and Governance in Vertex AI
- Features for data residency and access management
- Audit trails and traceability
- Practical exercise: setting up compliance policies
Scaling and Enterprise Integration
- Expanding Vertex AI deployments
- Connecting with corporate systems and APIs
- Practical exercise: enterprise-level deployment
Case Studies and Best Practices
- Success stories from finance, healthcare, and public sectors
- Insights from enterprise adoption experiences
- Guidelines for sustained operations
Wrap-up and Future Actions
Requirements
- Practical experience in deploying ML models to production environments
- Proficiency with CI/CD workflows
- Knowledge of data governance and regulatory frameworks
Target Audience
- MLOps engineers
- Platform teams
- Compliance officers
Testimonials (1)
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