This course introduces the artificial intelligence (AI) and machine learning (ML) offerings on Google Cloud that support the data-to-AI lifecycle through AI foundations, AI development, and AI solutions. It explores the technologies, products, and tools available to build an ML model, an ML pipeline, and a generative AI project based on the different goals of users, including data scientists, AI developers, and ML engineers.
Introduction to AI and Machine Learning on Google Cloud
Introduction to AI and Machine Learning on Google Cloud
This course is part of multiple programs.
Instructor: Google Cloud Training
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Gain insight into a topic and learn the fundamentals.
4.6
(283 reviews)
Beginner level
No prior experience required
Flexible schedule
8 hours to complete
Learn at your own pace
86%
Most learners liked this course
What you'll learn
Recognize the data-to-AI technologies and tools offered by Google Cloud.
Use generative AI capabilities in applications.
Choose between different options to develop an AI project on Google Cloud.
Build ML models end-to-end by using Vertex AI.
Skills you'll gain
Details to know
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Assessments
4 assignments
Taught in English
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- Gain a foundational understanding of a subject or tool
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- Earn a shareable career certificate
There are 6 modules in this course
This lesson guides learners through the course structure, which is built upon a three-layer AI framework: AI infrastructure, development, and solutions. It outlines the learning objectives and introduces learners to Google's comprehensive suite of full-stack AI development tools.
What's included
1 video
This module begins with a use case demonstrating the AI capabilities. It then focuses on the AI infrastructure like compute and storage. It also explains the primary data and AI development products on Google Cloud. Finally, it demonstrates how to use BigQuery ML to build an ML model, which helps transition from data to AI.
What's included
7 videos1 reading1 assignment1 app item
This module introduces generative AI (gen AI), the latest AI advancement, and the Google Cloud toolkits for developing gen AI projects. It starts by examining the foundation models. It then investigates the prompt-to-production lifecycle with Vertex AI Studio, including prompt engineering, app deployment, and model tuning. Additionally, this module explores AI agents and Google’s full stack of AI agent development tools.
What's included
8 videos1 reading1 assignment1 app item
This module explores the various options for developing an AI project on Google Cloud, from ready-made solutions like pre-trained APIs, to no-code and low-code solutions like AutoML, and code-based solutions like custom training. It compares the advantages and disadvantages of each option to help decide the right development tools.
What's included
7 videos1 reading1 assignment1 app item
This module walks through the ML workflow from data preparation, to model development, and to model serving on Vertex AI. It also illustrates how to convert the workflow into an automated pipeline using Vertex AI Pipelines.
What's included
8 videos1 reading1 assignment1 app item
This lesson summarizes the course by addressing the most important concepts, tools, technologies, and products for each module.
What's included
1 video2 readings
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4.7 (59 ratings)Offered by
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4.6
283 reviews
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Showing 3 of 283
JD
5
Reviewed on Aug 21, 2025
It's excellent with a lot of excellent additional reading to deep dive. Thanks
SS
5
Reviewed on Mar 13, 2025
Gave a very easy to understand introduction into all the relevant concepts of Machine Learning alongside hands on, guided lab exercises.
NK
5
Reviewed on Oct 17, 2024
Well-structured for beginners, it balances theory with hands-on exercises. While it could go deeper, it's an excellent springboard for further learning in AI/ML.
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