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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-centric features
- Application examples within regulated industries
Building Enterprise MLOps Pipelines
- Integration of Vertex AI into CI/CD workflows
- Techniques for automation and orchestration
- Practical lab: Constructing a deployment pipeline
Monitoring and Observability
- Implementing live model monitoring and alerting systems
- Developing model performance dashboards
- Practical lab: Configuring monitoring workflows
Grounding and Gen AI Evaluation
- Grounding models using enterprise data sources
- Leveraging Gen AI evaluation libraries and tools
- Practical lab: Executing evaluation workflows
Compliance and Governance with Vertex AI
- Features for data residency and access control
- Ensuring auditability and traceability
- Practical lab: Setting up compliance policies
Scaling and Enterprise Integration
- Methods for scaling Vertex AI deployments
- Integration with broader enterprise systems and APIs
- Practical lab: Executing an enterprise-scale deployment
Case Studies and Best Practices
- Success stories from financial services, healthcare, and the public sector
- Key lessons from enterprise adoption experiences
- Best practices for sustained long-term operations
Conclusion and Recommended Next Steps
Requirements
- Practical 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 engineering teams.
- Compliance leads.
Testimonials (1)
easy steps in ML