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Vertex AI for Machine Learning Practitioners

This instructor-led one-day course is designed for engineers and data scientists familiar with machine learning models who want to become procient in using Vertex AI for custom model workows.

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.

GK# 899025 Vendor# GCP-VERT-AI-ML
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Who Should Attend?

Machine Learning Engineers, Data Scientists

What You'll Learn

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

Course Outline

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
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Prerequisites

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