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
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Introduction to AI and Machine Learning on Google Cloud
This course is part of Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate
Instructor: Google Cloud Training
14,494 already enrolled
Included with 
Gain insight into a topic and learn the fundamentals.
4.6
(143 reviews)
Beginner level
No prior experience required
9 hours to complete
3 weeks at 3 hours a week
Flexible schedule
Learn at your own pace
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.
Details to know
Shareable certificate
Add to your LinkedIn profile
Assessments
4 assignments
Taught in English
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This course is part of the Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate
When you enroll in this course, you'll also be enrolled in this Professional Certificate.
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate from Google Cloud
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Add this credential to your LinkedIn profile, resume, or CV
Share it on social media and in your performance review
There are 6 modules in this course
This module covers the course objective of helping learners navigate the AI development tools on Google Cloud. It also provides an overview of the course structure, which is based on a three-layer AI framework including AI foundations, development, and solutions.
What's included
1 video
This module begins with a use case demonstrating the AI capabilities. It then focuses on the AI foundations including cloud 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
10 videos1 reading1 assignment1 app item
This module explores the various options for developing an ML 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
8 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
9 videos1 reading1 assignment1 app item
This module introduces generative AI (gen AI), the newest advancement in AI, and the essential toolkits for developing gen AI projects. It starts by examining the gen AI workflow on Google Cloud. It then investigates how to use Gen AI Studio and Model Garden to access Gemini multimodal, design prompt, and tune models. Finally, it explores the built-in gen AI capabilities of AI solutions.
What's included
9 videos1 reading1 assignment1 app item
This module provides a summary of the entire course by covering the most important concepts, tools, technologies, and products.
What's included
1 video1 reading
Instructor
Instructor ratings
4.8 (28 ratings)Offered by
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