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Orchestrate ML Workflows with Vertex AI Pipelines

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Overview

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Discover how to orchestrate ML workflows on Google Cloud. Explore the business drivers for orchestration and the technical architecture of Vertex AI Pipelines. Learn to create MLOps pipelines using a flexible, hybrid approach: utilize the no-code Template Gallery or construct custom workflows with the Kubeflow Pipelines (KFP) SDK and Google's pre-built components. Finally, accelerate your workflows using the Data Science Agent—an AI-powered collaborator that automates pipeline code generation.

Syllabus

  • Introduction
    • Course introduction
  • The Case for Orchestration
    • Business Problems that Drive Orchestration
    • Why We Need ML Workflows
    • What Is ML Orchestration?
    • Summary
    • Quiz
  • Introduction to Vertex AI Pipelines
    • Pipeline Architecture
    • Introduction to Vertex AI Pipelines
    • Methods for Developing Vertex AI Pipelines
    • Summary
    • Quiz
  • Authoring Pipelines
    • Using Google’s Pre-Built Components
    • Building KFP Lightweight Components
    • Optimize Your Workflow: The Hybrid Approach
    • Summary
    • Quiz
    • Building End to End MLOps Pipelines using Agent Platform
  • The Data Science Agent
    • Introduction to the Data Science Agent
    • Prompt Engineering for the Data Science Agent
    • Summary
    • Quiz
    • Streamline Data Science Workflows with the Colab Enterprise Agent
  • Course Summary and Next Steps
    • Course summary
    • Reading List
  • Your Next Steps
    • Claim credential

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