Sorry, this page is no longer available

We may earn an affiliate commission when you visit our partners.

Multimodal Retrieval Augmented Generation (RAG) using the Vertex AI Gemini API

Google Cloud Training

This is a self-paced lab that takes place in the Google Cloud console. In this lab, you learn how to perform multimodal retrieval augmented generation (RAG) using Vertex AI Gemini API.

Enroll now

Or subscribe to Coursera Plus

And get unlimited access to Coursera

Here's a deal for you

Save money when you learn with a deal that may be relevant to this course.

All coupon codes, vouchers, and discounts are applied automatically unless otherwise noted.

Valid until August 30

Google AI App Builder

Learn how to use Gemini API and API Studio with a three-course series from Google DeepMind

What's inside

Syllabus

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Introduces students to multimodal retrieval augmented generation (RAG), which is becoming more common in industry

Is taught by Google Cloud Training, who are recognized for their work in AI and machine learning

Is self-paced and takes place in the Google Cloud console, providing hands-on experience

Requires students to have some prior knowledge of AI and machine learning, which may be a barrier for some learners

Covers a specific aspect of AI and machine learning, which may not be of interest to all learners

Save this course

Create your own learning path. Save this course to your list so you can find it easily later.

Save

Reviews summary

Practical multimodal rag with gemini api

Learners say this course offers a highly practical and hands-on experience for implementing Multimodal Retrieval Augmented Generation (RAG) using the Vertex AI Gemini API. It provides clear steps within the Google Cloud console, making it an excellent resource for developing cutting-edge AI solutions. While focusing on direct application, it is best suited for those with some prior AI/ML knowledge, offering valuable insights into real-world RAG implementation.

Requires prior knowledge of AI/ML concepts and cloud fundamentals.

"This course is definitely for those already familiar with RAG and Google Cloud basics."

"I found it challenging without a solid foundation in machine learning before starting."

"It jumped straight into code, which was great, but not for absolute beginners."

Offers flexible, hands-on learning within the Google Cloud console.

"I liked the self-paced lab structure; it allowed me to learn at my own speed."

"Working directly in the Google Cloud console was a great way to learn by doing."

"The hands-on lab environment made the concepts much clearer."

Covers highly relevant topics in generative AI and multimodal models.

"The content on multimodal RAG and Gemini is incredibly relevant for my work."

"I appreciate learning about the latest advancements in AI from this course."

"This lab provided timely skills needed for today's AI development landscape."

Focuses on direct, hands-on application of Vertex AI Gemini API.

"I gained practical experience working with the cutting-edge Gemini API for RAG."

"The lab provided clear steps to implement multimodal RAG within Google Cloud."

"It was great to apply theoretical RAG knowledge directly in a real cloud environment."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Multimodal Retrieval Augmented Generation (RAG) using the Vertex AI Gemini API with these activities:

Review previous coursework or study materials on natural language processing

Show steps

Refresh your memory and reacquaint yourself with the basics of natural language processing before embarking on this course.

Browse courses on Natural Language Processing

Show steps

Gather relevant materials
Review key concepts

Review Python programming concepts

Show steps

Review basic Python programming concepts to strengthen your foundational understanding before starting the course, ensuring a smoother learning experience.

Browse courses on Python

Show steps

Revisit Python data types, variables, and operators.
Refresh your knowledge of Python control flow (e.g., if-else statements, loops).
Practice writing simple Python functions and modules.

Review foundational materials on natural language processing

Show steps

Enhance your understanding by reviewing a foundational book on natural language processing to strengthen your grasp of core concepts before delving into the course material.

View Natural Language Processing with Python:... on Amazon

Show steps

Obtain the book
Read and comprehend key chapters

Six other activities

Expand to see all activities and additional details

Show all nine activities

Test your understanding of Multimodal Retrieval Augmented Generation (RAG)

Show steps

Reinforce your understanding of RAG by solving practice problems and testing your skills.

Browse courses on RAG

Show steps

Review the course materials on RAG.
Complete the practice exercises provided in the course.
Seek out additional practice problems online or in textbooks.

Follow tutorials on Vertex AI Gemini API

Show steps

Enhance your understanding of the Vertex AI Gemini API through guided tutorials, deepening your knowledge of multimodal retrieval augmented generation.

Browse courses on AI

Show steps

Locate and enroll in relevant tutorials on the official Vertex AI documentation website.
Follow the tutorials step-by-step, implementing the code and experimenting with the API.
Explore additional resources and documentation to expand your knowledge.

Build a small-scale RAG application

Show steps

Solidify your understanding by applying your knowledge to a practical project, building a small-scale RAG application that demonstrates your comprehension of the concepts.

Show steps

Design the application, including the user interface and data flow.
Implement the RAG functionality using the Vertex AI Gemini API.
Test and refine your application to ensure it meets the desired requirements.
Optionally, deploy your application to a platform or share it with others.

Explore advanced RAG techniques with guided tutorials

Show steps

Expand your knowledge of RAG by following guided tutorials that cover advanced concepts and techniques.

Browse courses on RAG

Show steps

Identify specific areas of RAG you want to improve.
Search for high-quality tutorials that align with your learning goals.
Follow the tutorials step-by-step, taking notes and experimenting with the code.
Apply what you've learned to practical examples or projects.

Contribute to open-source projects related to multimodal retrieval augmented generation (RAG)

Show steps

Deepen your understanding and support the community by contributing to open-source projects that focus on multimodal retrieval augmented generation (RAG).

Browse courses on Open Source

Show steps

Identify suitable open-source projects
Review project documentation
Make meaningful contributions

Build a RAG-based application to solve a real-world problem

Show steps

Solidify your understanding of RAG by applying it to a practical problem and creating a tangible deliverable.

Browse courses on RAG

Show steps

Define a problem that can be addressed with RAG.
Design and develop a RAG-based solution.
Implement and test your solution.
Present your application and its results.

Career center

Learners who complete Multimodal Retrieval Augmented Generation (RAG) using the Vertex AI Gemini API will develop knowledge and skills that may be useful to these careers: