Machine Learning on Google Cloud

The Machine Learning on Google Cloud course is designed to teach students how to build, train, and deploy machine learning models using Google’s Vertex AI platform, without writing a single line of code. During the course, students will learn how to create models with Vertex AI AutoML and BigQuery ML, manage datasets with Vertex AI Managed Datasets, and add features to the Feature Store. They will be introduced to tools such as Analytics Hub, Dataplex, and Data Catalog, and learn how to use parameter tuning using Vertex Vizier to improve model performance. The course covers creating notebooks managed by Vertex AI Workbench and preparing custom training jobs using Docker containers. Students will also gain knowledge of batch and online predictions, model monitoring, and data quality. Key technologies covered include TensorFlow, Keras, tf.data, and Keras APIs for building models. The course helps prepare students for the Google Professional Machine Learning Engineer Certification exam .

Submit a request

Course Objectives

Below is a summary of the main objectives of the Machine Learning on Google Cloud Course :

Build, train, and deploy machine learning models without writing code using Vertex AI AutoML.
Use BigQuery ML to build and train models with SQL.
Manage datasets and features with Vertex AI Managed Datasets and Feature Store.
Optimize models by tuning parameters with Vertex Vizier.
Implement scalable machine learning models using TensorFlow and Keras.
Integrate machine learning models into applications with Vertex AI endpoints.
Monitor and evaluate model performance using Google Cloud’s tools.
Implement best practices for machine learning model lifecycle management.

Course Certification

This course helps you prepare to take the:
Google Cloud Certified Professional Machine Learning Engineer Exam;

Course Outline

Module 1: Introduction to AI and Machine Learning on Google Cloud

AI Foundations
AI Development Options
AI Development Workflow
Generative AI

Module 2: Launching into Machine Learning

Improve data quality
Exploratory Data Analysis
Regression
Classification
Train AutoML Models using Vertex AI
Optimization
Train AutoML Models using BQML
Generalization
Sampling

Module 3: TensorFlow on Google Cloud

Design and build a TensorFlow 2.x input data pipeline
Build, train and deploy machine learning models using TF/Keras
keras preprocessing layers

Module 4: Feature Engineering

Purpose of feature engineering and how it helps to improve model performance
Introduction to Vertex AI Feature Store

Module 5: Machine Learning in the Enterprise

Understanding the ML Enterprise Workflow
Data in the Enterprise; Data Management & Governance
Data Preprocessing Options
Training ML models using Vertex AI AutoML
Science of ML and Custom Training
Vizier Hyperparameter tuning
Building a Vertex AI Pipeline

Course Mode

Instructor-Led Remote Live Classroom Training;

Trainers

Trainers are GCP Official Instructors and certified in other IT technologies, with years of hands-on experience in the industry and in Training.

Lab Topology

For all types of delivery, the Trainee can access real Cisco equipment and systems in our laboratories or directly at the Cisco data centers remotely 24 hours a day. Each participant has access to implement the various configurations thus having a practical and immediate feedback of the theoretical concepts.
Here are some Labs topologies available:

Course Details

Course Prerequisites

Attendance at the Google Cloud Big Data and Machine Learning Fundamentals course is recommended .

Course Duration

Intensive duration 5 days

Course Frequency

Course Duration: 5 days (9.00 to 17.00) - Ask for other types of attendance.

Course Date

Machine Learning on Google Cloud Course (Intensive Formula) – On request – 09:00 – 17:00

Steps to Enroll

Registration takes place by asking to be contacted from the following link, or by contacting the office at the international number +355 45 301 313 or by sending a request to the email info@hadartraining.com