Orchestrate ML Workflows with Vertex AI Pipelines

Orchestrate ML Workflows with Vertex AI Pipelines

Instructor: Google Cloud Training

Access provided by HAECO Americas

6 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Some related experience required

4 hours to complete

Flexible schedule

Learn at your own pace

6 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Some related experience required

4 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Explain the use cases that drive the adoption of ML Orchestration.
Describe how Vertex AI drives MLOps automation, reproducibility, and scaling.
Implement production-grade pipelines using Vertex AI’s no-code Template Gallery.
Build hybrid pipeline workflows with Kubeflow and pre-built GCP components.

Details to know

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Assessments

4 assignments

Taught in English

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There are 6 modules in this course

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.

This lesson guides learners through the course structure, which is built upon the transition from ad-hoc experimentation to robust, production-grade systems using Vertex AI Pipelines . It outlines the strategies for ML orchestration—ranging from no-code to hybrid pipelines—and introduces learners to the Data Science Agent for accelerating the automation of complex, deployable workflows .

What's included

1 video

Examine the operational bottlenecks complex ML processes and determine the need for automated reproducible workflows orchestration.

What's included

4 videos1 assignment

Explore Vertex AI and the core mechanics of ML pipelines, including compilers, DAGs, runners, artifact passing, and metadata lineage.

What's included

4 videos1 assignment

Optimize ML workflows using the "Hybrid" pipeline strategy. Evaluate specific workflow requirements to determine the balance between using Google’s validated Pre-built Component and creating custom Lightweight Python Components for proprietary logic.

What's included

4 videos1 assignment1 app item1 plugin

4 videosTotal 22 minutes

Using Google’s Pre-Built Components7 minutes
Building KFP Lightweight Components7 minutes
Optimize Your Workflow: The Hybrid Approach5 minutes
Summary3 minutes

1 assignmentTotal 6 minutes

Quiz-36 minutes

1 app itemTotal 60 minutes

Lab: Building End to End MLOps Pipelines using Vertex AI60 minutes

1 pluginTotal 15 minutes

Accessing and completing labs15 minutes

Leverage the Data Science Agent to automate code generation and troubleshoot architectural errors using the Context-Task-Constraint (CTC) prompt engineering framework.

What's included

3 videos1 assignment1 app item

This lesson summarizes the course by addressing the transition from ad-hoc notebooks to robust, production-grade systems using Vertex AI Pipelines . It reviews the core concepts of ML Orchestration and Hybrid Pipelines, highlights tools like Google's pre-built components and the Kubeflow SDK, and recaps technologies such as the Data Science Agent for automating complex challenges like media sales forecasting and customer churn prediction .