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Course Outline
Introduction to Vertex AI for Enterprise Use
- Key AI requirements and challenges in enterprise settings
- Overview of Vertex AI enterprise capabilities
- Applications in highly regulated industries
Establishing Enterprise MLOps Pipelines
- Connecting Vertex AI with CI/CD workflows
- Process automation and orchestration
- Practical exercise: Constructing a deployment pipeline
Monitoring and Observability
- Real-time model monitoring and alerting mechanisms
- Performance dashboards for models
- Practical exercise: Configuring monitoring workflows
Grounding and Generative AI Evaluation
- Grounding models using enterprise data
- Libraries and tools for generative AI assessment
- Practical exercise: Deploying evaluation workflows
Compliance and Governance within Vertex AI
- Features for data residency and access management
- Ensuring auditability and traceability
- Practical exercise: Setting up compliance policies
Scaling and Enterprise Integration
- Expanding Vertex AI deployments
- Integrating with enterprise systems and APIs
- Practical exercise: Executing enterprise-scale deployments
Case Studies and Best Practices
- Success stories from financial services, healthcare, and the public sector
- Insights gained from enterprise adoption
- Best practices for sustained operations
Recap and Future Steps
Requirements
- Practical experience deploying ML models in production environments
- Working knowledge of CI/CD pipelines
- Understanding of data governance and compliance frameworks
Target Audience
- MLOps engineers
- Platform engineering teams
- Compliance officers
14 Hours
Testimonials (1)
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