Course Catalog
Vertex AI for Machine Learning Practitioners
Code: GCP-VERT-AI-ML
Duration: 1 Day
$900 USD

OVERVIEW

This practical, hands-on course will provide you with a deep dive into the core functionalities of Vertex AI, enabling you to effectively leverage its tools and capabilities for your ML projects.

DELIVERY FORMAT

This course is available in the following formats:

Virtual Classroom

Duration: 1 Day

CLASS SCHEDULE

Delivery Format: Virtual Classroom
Date: Sep 14 2026 - Sep 14 2026 | 09:00 - 17:00 EST
Location: Online
Course Length: 1 Day

$ 900

GOALS

By the end of the course, learners will be able to:

  • Understand the key components of Vertex AI and how they work together to support your ML workows.
  • Congure and launch Vertex AI Custom Training and Hyperparameter Tuning Jobs to optimize model performance.
  • Organize and version your models using Vertex AI Model Registry for easy access and tracking.
  • Congure serving clusters and deploy models for online predictions with Vertex AI Endpoints.
  • Operationalize and orchestrate end-to-end ML workows with Vertex AI Pipelines for increased eciency and scalability.
  • Configure and set up monitoring on deployed models
OUTLINE


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Will Be Updated Soon!

Module 1: Training, Tuning, and Deploying Models on Vertex AI

  • Understand Containerized Training Applications
  • Understand Vertex AI Custom Training and Tuning Jobs
  • Understand how to track and version your trained models in Vertex AI Model Registry
  • Understand Online Deployment with Vertex AI Endpoints

Module 2:Orchestrating end-to-end Workows with Vertex AI Pipelines

  • Understand Kubeow
  • Understand pre-built and lightweight Python components
  • Understand how to compile and execute pipelines on Vertex AI

Module 3: Model Monitoring on Vertex AI

  • Understand Feature Drift and Skew
  • Understand Model Monitoring for models deployed to Vertex AI Endpoints
LABS


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Will Be Updated Soon!
Will Be Updated Soon!
WHO SHOULD ATTEND

Machine Learning Engineers, Data Scientists

PREREQUISITES

Experience building and training custom ML models. Familiar with Docker.