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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
- Application scenarios in regulated industries
Establishing Enterprise MLOps Pipelines
- Integrating Vertex AI into CI/CD workflows
- Strategies for automation and orchestration
- Hands-on lab: Constructing a deployment pipeline
Monitoring and Observability
- Implementing live model monitoring and alerting systems
- Creating model performance dashboards
- Hands-on lab: Configuring monitoring workflows
Grounding and Gen AI Evaluation
- Grounding models using enterprise data sources
- Utilizing Gen AI evaluation libraries and tools
- Hands-on lab: Setting up 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 for high volume
- Integrating with broader enterprise systems and APIs
- Hands-on lab: Executing enterprise-scale deployment
Case Studies and Best Practices
- Success stories from financial services, healthcare, and the public sector
- Insights gained from enterprise adoption experiences
- Best practices for sustaining long-term operations
Summary and Future Steps
Requirements
- Experience in deploying ML models to 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)
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