Introduction to AI and Machine Learning on Google Cloud
The Introduction to AI and Machine Learning on Google Cloud course is designed for AI developers, data scientists, and ML engineers who want to build predictive and generative AI projects using Google Cloud. During the course, participants will explore the technologies and tools available across the data to AI lifecycle, including AI foundations, development, and solutions. Technologies such as Vertex AI, Gemini multimodal, AutoML, BigQuery ML, Vertex AI Pipelines, TensorFlow, Model Garden, Vertex AI Studio, and the Natural Language API will be covered. Participants will learn to build end-to-end ML models using Vertex AI and develop generative AI projects with Gemini multimodal. This course helps prepare for the Google Cloud AI Engineer Certification exam. This course helps prepare for the Google Professional Machine Learning Engineer Certification exam .
Course Objectives
Below is a summary of the main objectives of the Introduction to AI and Machine Learning on Google Cloud Course:
- Recognize the data-to-AI technologies and tools provided by Google Cloud.
- Building generative AI projects using Gemini multimodal, efficient prompts, and model tuning.
- Explore various options for developing an AI project on Google Cloud.
- Build an end-to-end ML model using Vertex AI.
- Using AutoML on Vertex AI for your AI development workflow.
- Understand the fundamentals of AI and machine learning.
- Learn how to preprocess and prepare data for machine learning.
- Gain practical experience in deploying and managing AI models on Google Cloud.
Course Certification
This course helps you prepare to take the:
Google Cloud Certified Professional Machine Learning Engineer Exam;
Course Outline
Module 01 – AI Foundations
• Why AI?
• AI/ML framework on Google Cloud
• Google Cloud infrastructure
• Data and AI products
• ML model categories
• BigQuery ML
• Lab introduction: BigQuery ML
• Recognize the AI/ML framework on Google Cloud.
• Identify the major components of Google Cloud infrastructure.
• Define the data and ML products on Google Cloud and how they support the data-to-AI lifecycle.
• Build an ML model with BigQueryML to bring data to AI.
• Lab: Predicting Visitor Purchases with BigQuery ML
Module 02 – AI Development Options
• AI development options
• Pre-trained APIs
• Vertex AI
• AutoML
• Custom training
• Lab introduction: Natural Language API
• Define different options to build an ML model on Google Cloud.
• Recognize the primary features and applicable situations of pre-trained APIs, AutoML, and custom training.
• Use the Natural Language API to analyze text.
• Lab: Entity and Sentiment Analysis with Natural Language API
Module 03 – AI Development Workflow
• ML workflow
• Data preparation
• Model development
• Model serving
• MLOps and workflow automation
• Lab introduction: AutoML
• How a machine learns
• Define the workflow of building an ML model.
• Describe MLOps and workflow automation on Google Cloud.
• Build an ML model from end to end using AutoML on Vertex AI.
• Lab: Vertex AI: Predicting Loan Risk with AutoML
Module 04 – Generative AI
• Generative AI and workflow
• Gemini multimodal
• Prompt design
• Model tuning
• Model Garden
• AI solutions
• Lab introduction: Vertex AI Studio
• Define generative AI and foundation models.
• Use Gemini multimodal with Vertex AI Studio.
• Design efficient prompt and tune models with different methods.
• Recognize the AI solutions and the embedded Gen AI features.
• Lab: Getting Started with Vertex AI Studio
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
Basic understanding of cloud computing concepts, familiarity with IT infrastructure, and some experience with Google Cloud Platform or another cloud provider..
Course Duration
Intensive duration 1 days
Course Frequency
Course Duration: 1 days (9.00 to 17.00) - Ask for other types of attendance.
Course Date
- ntroduction to AI and 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
