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Course Outline

Introductory Overview: Vertex AI for Enterprise Use

  • Key AI requirements and challenges in enterprise settings.
  • High-level review of Vertex AI's enterprise-specific features.
  • Application of AI in highly regulated industries.

Configuring Enterprise MLOps Pipelines

  • Seamlessly integrating Vertex AI into CI/CD workflows.
  • Strategies for automation and task orchestration.
  • Practical session: constructing an end-to-end deployment pipeline.

Monitoring and Observability Practices

  • Implementing live model monitoring and automated alerting.
  • Designing and interpreting model performance dashboards.
  • Practical session: establishing comprehensive monitoring workflows.

Grounding Strategies and Gen AI Evaluation

  • Anchorinng models using proprietary enterprise data.
  • Leveraging dedicated evaluation libraries and tools for Gen AI.
  • Practical session: executing and validating evaluation workflows.

Compliance and Governance within Vertex AI

  • Managing data residency and access control mechanisms.
  • Ensuring auditability and full traceability of actions.
  • Practical session: setting up and verifying compliance policies.

Scaling for Enterprise Integration

  • Techniques for scaling Vertex AI deployments effectively.
  • Connecting with broader enterprise systems and APIs.
  • Practical session: managing an enterprise-scale deployment scenario.

Case Studies and Industry Best Practices

  • Success stories from financial services, healthcare, and the public sector.
  • Key lessons learned from enterprise-wide AI adoption.
  • Recommendations for sustainable long-term operations.

Recap and Future Directions

Requirements

  • Proven experience in deploying ML models to production environments.
  • Proficiency with CI/CD pipeline workflows.
  • A solid grasp of data governance principles and compliance frameworks.

Intended Audience

  • MLOps Engineers
  • Platform Engineering Teams
  • Compliance Officers and Leads
 14 Hours

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