GCP200VERTEXAI

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 proficient in using Vertex AI for custom model workflows. 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.

✓ Formation officielle Google CloudNiveau Intermediate⏱️ 1 jour (7h)

Ce que vous allez apprendre

Understand the key components of Vertex AI and how they work together to support your ML workflows.
Configure 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.
Configure serving clusters and deploy models for online predictions with Vertex AI Endpoints.
Operationalize and orchestrate end-to-end ML workflows with Vertex AI Pipelines for increased efficiency and scalability.
Configure and set up monitoring on deployed models

Prérequis

Experience building and training custom ML models
Familiar with Docker

Public cible

Machine Learning Engineers, Data Scientists

Programme de la Formation

3 modules pour maîtriser les fondamentaux

Sujets abordés

→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

Quality Process

SFEIR Institute's commitment: an excellence approach to ensure the quality and success of all our training programs. Learn more about our quality approach

Teaching Methods Used

Lectures / Theoretical Slides — Presentation of concepts using visual aids (PowerPoint, PDF).
Technical Demonstration (Demos) — The instructor performs a task or procedure while students observe.
Guided Labs — Guided practical exercises on software, hardware, or technical environments.
Quiz / MCQ — Quick knowledge check (paper-based or digital via tools like Kahoot/Klaxoon).

Evaluation and Monitoring System

The achievement of training objectives is evaluated at multiple levels to ensure quality:

Continuous Knowledge Assessment : Verification of knowledge throughout the training via participatory methods (quizzes, practical exercises, case studies) under instructor supervision.
Progress Measurement : Comparative self-assessment system including an initial diagnostic to determine the starting level, followed by a final evaluation to validate skills development.
Quality Evaluation : End-of-session satisfaction questionnaire to measure the relevance and effectiveness of the training as perceived by participants.