Introduction to AI and Machine Learning on Google Cloud

Choosing an AI service is easier when you understand the project behind it. Google’s Introduction to AI and Machine Learning on Google Cloud connects the main stages of AI development, from preparing data to delivering predictions and building generative applications. It gives newcomers a way to see how the pieces fit together before committing to a particular tool.

The course brings predictive machine learning and generative AI into the same learning journey. Rather than concentrating on one model or one prompt, its curriculum moves through infrastructure, development choices, and the workflow that turns an idea into a usable application. That makes it relevant to developers exploring AI, analysts expanding their vocabulary, and technical teams comparing approaches for a project.

Course at a glance

What you’ll learn

• Place compute, storage, data services, and models within a data-to-AI project.
• Understand how BigQuery ML connects analytical data with predictive modeling.
• Explore foundation models, prompting, deployment, and model tuning in generative AI.
• Compare ready-made APIs, low-code development, and custom model training.
• Follow the stages of preparing data, developing a model, serving predictions, and automating the workflow.
• Recognize where AI agents fit into application development.

Skills you’ll gain

• AI project planning
• Machine learning fundamentals
• Generative AI
• Development option comparison
• ML workflow literacy
• Prompting concepts

From an AI idea to a development approach

The sequence begins with foundations, then introduces generative AI before examining the available development options. This order helps you separate the problem you want to solve from the product you might use. A ready-made API, an automatically trained model, and a custom training workflow answer different needs; comparing them is more useful than treating every AI project as the same task.

Later material follows the development lifecycle and introduces workflow automation. The listed exercises cover examples such as purchase prediction, language analysis, and model development. These are learning examples, not evidence that a model is ready for a real business decision. Keep the purpose of an exercise separate from the reliability, data quality, and testing your own application would require.

Who should consider this course?

Consider it if you can describe a useful AI application but are unsure how to break the work into manageable stages. It can also help a developer or analyst participate more confidently in discussions about models, data preparation, and delivery. The beginner classification makes it a starting point for the platform, rather than a substitute for deeper study in statistics or software engineering.

Before beginning, write down one problem and the information available to solve it. For example, choose between predicting a numeric outcome and generating a response from a document collection. As you study, note which course concepts help you explain that distinction. This gives the terminology a concrete purpose without requiring you to deploy a paid cloud service.

Study access and practical activities

You can watch all 31 teaching videos and read all six course documents for free with a Google Skills learner account. This study route does not require a paid subscription, payment card, or trial. The four hosted labs have separate access conditions and may require credits or a subscription. Using cloud products in your own project can also involve charges; course access does not make an independent deployment free.

Reading or watching teaching material is different from meeting every requirement for formal course completion. This listing does not promise a free badge, certificate, or professional certification. Review the access and completion conditions displayed by Google before choosing a study route.

For a useful study record, create a one-page project map with four boxes: data, development option, evaluation, and delivery. Add questions rather than invented performance targets. Revisit that map after the workflow section and identify the parts that still need specialist learning. You can also browse the course library to compare related subjects.

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Frequently asked questions