Introduction to AI and Machine Learning on Google Cloud

    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.

    Objectives

    In this course, participants will learn the following skills:

    • 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.

    Audience

    This course is intended for the following participants:

    • Professional AI developers, data scientists, and ML engineers who want to build predictive and generative AI projects on Google Cloud.

    Prerequisites

    To get the most of out of this course, participants should have:

    • Basic knowledge of machine learning concepts
    • Prior experience with programming languages such as SQL and Python

    Duration

    1 day

    Investment

    Check the next open public class in our enrollment page. If you are interested in a private training class for your company, contact-us.

    Introduction to AI and Machine Learning on Google Cloud dependencies with other courses and certifications
    Introduction to AI and Machine Learning on Google Cloud dependencies with other courses and certifications

    Course Outline

    • Define the course goal.
    • Recognize the course 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 BigQueryML to bring data to AI.
    • 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.
    • Define the workflow of building an ML model.
    • Describe MLOps and workflow automation on Google Cloud.
    • Build an ML model from end to end by using AutoML on Vertex AI.
    • 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.
    • Recognize the primary concepts, tools, technologies, and products learned in the course.
    Check our schedule and enroll