Course Overview of Introduction to AI and Machine Learning on Google Cloud

This instructor-led course introduces Google Cloud’s Artificial Intelligence and Machine Learning ecosystem with a focus on building both predictive and Generative AI solutions. Participants will explore Google Cloud technologies, products, and tools used throughout the complete data-to-AI lifecycle. 

The course covers AI foundations, BigQuery ML, Generative AI concepts, prompt engineering, AI agents, Vertex AI, AutoML, Natural Language API, and end-to-end ML workflows. Through hands-on labs, guided exercises, quizzes, and real-world use cases, learners will gain practical experience in developing, deploying, and managing AI and ML applications using Google Cloud services.  

After completing Introduction to AI and Machine Learning on Google Cloud, participants will be able to:

  • Recognize the data-to-AI technologies and tools offered by Google Cloud
  • Understand AI and ML fundamentals on Google Cloud
  • Build ML models using BigQuery ML and Vertex AI
  • Understand Generative AI concepts and foundation models
  • Build Generative AI projects using Gemini multimodal tools
  • Apply effective prompt engineering techniques
  • Explore AI agent development using Vertex AI Agent Builder
  • Choose the right Google Cloud AI development approach
  • Implement end-to-end ML workflows using Vertex AI
  • Understand MLOps and workflow automation concepts

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Key Features of Introduction to AI and Machine Learning on Google Cloud:

  •  AI and Machine Learning Foundations 

     

  • Hands-On Learning Experience 

     

  • Generative AI and Gemini Coverage 

     

  •  End-to-End ML Workflow 

     

  • Multiple AI Development Approaches 

     

  • Google Cloud AI Services Exposure 

     

  • AI Agent and GenAI Development 

     

  • Practical Enterprise Use Cases 

     

Who should Attend Introduction to AI and Machine Learning on Google Cloud?

  • AI Developers
  • Data Scientists
  • Machine Learning Engineers
  • Software Developers
  • Cloud Engineers
  • Data Analysts
  • AI Enthusiasts
  • Professionals interested in AI and Generative AI on Google Cloud

Prerequisites of Introduction to AI and Machine Learning on Google Cloud:

  • Basic understanding of machine learning concepts
  • Prior experience with SQL and Python programming languages
  • Familiarity with cloud concepts is beneficial
  • Interest in AI, ML, and Generative AI technologies

    • Why choose CloudThat as your training partner for Introduction to AI and Machine Learning on Google Cloud?

    • Specialized Google Cloud Expertise - CloudThat specializes in cloud and AI technologies, offering focused and industry-aligned Google Cloud training programs with real-world implementation experience. 
    •  Industry-Recognized Trainers  - Our trainers are certified Google Cloud professionals with expertise in AI, ML, Data Engineering, and Generative AI solutions. 
    • Hands-On Learning Approach - CloudThat emphasizes practical learning through guided labs, exercises, demonstrations, and enterprise-focused AI implementation scenarios. 
    • Customized Learning Paths - We offer tailored learning journeys suitable for beginners, developers, data professionals, and enterprise AI practitioners.
    •  Interactive Learning Experience - Training includes live demonstrations, discussions, quizzes, architecture walkthroughs, and practical implementation exercises.
    • Career and Certification Support - CloudThat supports learners with project guidance, interview preparation, and industry-focused AI and cloud career learning paths. 
    • Continuously Updated Content - Our course content is regularly updated to align with the latest advancements in Google Cloud AI, Vertex AI, Gemini, and Generative AI technologies. 
    • Trusted by Enterprises Worldwide - Thousands of learners and enterprises trust CloudThat for advanced cloud, AI, and Machine Learning training programs. 

    Learning Objective of Introduction to AI and Machine Learning on Google Cloud

    • Understand AI, Machine Learning, and Generative AI concepts on Google Cloud
    • Learn the complete data-to-AI lifecycle and ML workflow
    • Build ML models using BigQuery ML and Vertex AI
    • Explore foundation models, Gemini, and prompt engineering concepts
    • Understand AI agent development and Vertex AI Agent Builder
    • Compare pre-trained APIs, AutoML, and custom model development approaches
    • Implement ML workflows using Vertex AI and AutoML
    • Learn MLOps and workflow automation concepts
    • Build AI-powered applications using Google Cloud AI services
    • Apply best practices for AI and ML solution development 

    Course Outline of Introduction to AI and Machine Learning on Google Cloud: Download Course Outline

    Lecture Content

    • Course Introduction
    • Course Objectives
    • Learning Outcomes
    • Course Structure and Roadmap
    • Understanding the Data-to-AI Lifecycle

    Lab Content

    • NA

    Lecture Content

    • AI and Machine Learning on Google Cloud
    • AI Infrastructure Overview
    • AI Models and ML Concepts
    • Data and ML Products on Google Cloud
    • Introduction to BigQuery ML
    • AI/ML Framework on Google Cloud
    • Understanding AI Use Cases

    Lab Content

    • Lab: Predict Visitor Purchases with BigQuery ML

    Additional Activities

    • Quiz
    • Reading Assignments

    Lecture Content

    • AI Development Options on Google Cloud
    • Vertex AI Overview
    • AutoML and Low-Code AI Development
    • Pre-trained APIs
    • Custom Training Approaches
    • Tool Selection Strategies
    • AI Development Best Practices

    Lab Content

    • Lab: Entity and Sentiment Analysis with Natural Language API

    Additional Activities

    • Quiz
    • Reading Assignments

    Lecture Content

    • Machine Learning Workflow
    • Data Preparation and Feature Engineering
    • Model Development and Trainin
    • Model Serving and Deployment
    • MLOps and Workflow Automation
    • Vertex AI Pipelines
    • How Machine Learning Models Learn (Optional)
    • ML Lifecycle Management

    Lab Content

    • Lab: Vertex AI – Predict Loan Risk with AutoML

    Additional Activities

    • Quiz
    • Reading Assignments

    Lecture Content

    • Course Summary and Recap
    • Review of AI and ML Concepts
    • Google Cloud AI Products Overview
    • Best Practices and Recommendations
    • Career Learning Paths
    • Final Discussion and Q&A

    Lab Content

    • Final Discussion / Wrap-up

    Additional Activities

    • Reading Assignments

    Certification Details of Introduction to AI and Machine Learning on Google Cloud:

      Course Completion Certificate

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    Course ID: 28408

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    FAQs for Introduction to AI and Machine Learning on Google Cloud:

    This course is designed for AI developers, data scientists, ML engineers, developers, and professionals interested in AI and Machine Learning on Google Cloud.

    The course covers AI foundations, Generative AI, Vertex AI, Gemini, BigQuery ML, AutoML, AI agents, prompt engineering, ML workflows, and MLOps concepts.

    Basic understanding of Python and SQL is recommended.

    The course duration is approximately 1 day (480 minutes).

    Yes, the course includes hands-on labs for BigQuery ML, Vertex AI Studio, Natural Language API, and AutoML workflows.

    Yes, the course includes Generative AI concepts, Gemini, prompt engineering, AI agents, and Vertex AI Studio.

    The course includes Vertex AI, Vertex AI Studio, Vertex AI Agent Builder, Vertex AI Pipelines, BigQuery ML, Natural Language API, AutoML, Gemini Enterprise, and NotebookLM.

    Yes, learners will build ML models using BigQuery ML and AutoML with Vertex AI.

    Yes, the course introduces MLOps, workflow automation, and Vertex AI Pipelines.

    Yes, this is a beginner-level course designed for learners starting their AI and ML journey on Google Cloud.

    Enquire Now