Did you know that the adoption of machine learning results in 2x more data-driven decisions, 5x faster decision-making, and 3x faster execution?
Learn how to implement the latest machine learning and artificial intelligence technology with courses on Vertex AI, BigQuery, TensorFlow, and more. Boost your AI skills to take your career to the next level or to prepare for a role in machine learning or software development.
Overview
This course introduces the AI and machine learning (ML) offerings on Google Cloud that build both predictive and generative AI projects.
It explores the technologies, products, and tools available throughout the data-to-AI life cycle, encompassing AI foundations, development, and solutions. It aims to help data scientists, AI developers, and ML engineers enhance their skills and knowledge through engaging learning experiences and practical hands-on exercises.
Skills Covered
- Recognize the data-to-AI technologies and tools provided by Google Cloud
- Build generative AI projects by using Gemini multimodal, efficient prompts, and model tuning
- Explore various options for developing an AI project on Google Cloud
- Create an ML model from end-to-end by using Vertex AI.
Prerequisites
- Basic knowledge of machine learning concepts
- Prior experience with programming languages such as SQL and Python
Target Audience
Professional AI developers, data scientists, and ML engineers who want to build predictive and generative AI projects on Google Cloud
Module 0: Course Introduction
- Define the course goal.
- Recognize the course objectives.
Module 1: AI Foundations
Topics
- Why AI?
- AI/ML framework on Google Cloud
- Google Cloud infrastructure
- Data and AI products
- ML model categories
- BigQuery ML
- Lab introduction: BigQuery ML
Learning Objectives
- 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 BigQuery ML to bring data to AI.
Lab
- Lab: Predicting Visitor Purchases with BigQuery ML
Assessment
- Quiz
Module 2: AI Development Options
Topics
- AI development options
- Pre-trained APIs
- Vertex AI
- AutoML
- Custom training
- Lab introduction: Natural Language API
Learning Objectives
- 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
- Lab: Entity and Sentiment Analysis with Natural Language API
Assessment
- Quiz
Module 3: AI Development Workflow
Topics
- ML workflow
- Data preparation
- Model development
- Model serving
- MLOps and workflow automation
- Lab introduction: AutoML
- How a machine learns
Learning Objectives
- Define the workflow of building an ML model.
- Describe MLOps and workflow automation on Google Cloud.
- Build an ML model end-to-end using AutoML on Vertex AI.
Lab
- Lab: Vertex AI: Predicting Loan Risk with AutoML
Assessment
- Quiz
Module 4: Generative AI
Topics
- Generative AI and workflow
- Gemini multimodal
- Prompt design
- Model tuning
- Model Garden
- AI solutions
- Lab introduction: Vertex AI Studio
Learning Objectives
- Define generative AI and foundation models.
- Use Gemini multimodal with Vertex AI Studio.
- Design effective prompts and tune models using different methods.
- Recognize AI solutions and embedded generative AI features.
Lab
- Lab: Getting Started with Vertex AI Studio
Assessment
- Quiz
Module 5: Course Summary
- Recognize the primary concepts, tools, technologies, and products covered in the course.
Dates & Locations
August 3, 2026 - August 3, 2026
August 3, 2026 - August 3, 2026
November 16, 2026 - November 16, 2026
November 16, 2026 - November 16, 2026
Exam & Certification
Google Certified Associate Data Practitioner.
The Associate Data Practitioner secures and manages data on Google Cloud. This individual has experience using Google Cloud data services for tasks like data ingestion, transformation, pipeline management, analysis, machine learning, and visualization. Candidates have a basic understanding of cloud computing concepts like infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS).
This course along with GCP-IDEGC: Introduction to Data Engineering on Google Cloud prepares you for the Associate Data Practitioner certification.
Training & Certification Guide
Frequently Asked Questions
Speak to a Training Consultant
All courses are HRD Claimable.
Get in touch with our team via the form or WhatsApp us on +6011-5119 6631
