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Multimodal Use Cases with Gemini 1.5

This is a self-paced lab that takes place in the Google Cloud console. In this lab, you will learn how to use Gemini 1.5 Pro and Gemini 1.5 Flash LLMs for multimodal use cases.

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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

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what should give you pause

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Focuses on Gemini 1.5 Pro and Gemini 1.5 Flash LLMs, which are cutting-edge tools for building advanced AI applications

Offered by Google Cloud, a leading provider of cloud computing services and AI technologies, ensuring practical and relevant instruction

Emphasizes multimodal use cases, which are increasingly important for creating versatile and intelligent systems that can process various data types

Requires access to the Google Cloud console, which may necessitate a Google Cloud account and familiarity with cloud environments

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Reviews summary

Hands-on lab for multimodal gemini 1.5

According to learners, this course is a highly recommended, practical and hands-on lab focused on multimodal use cases with Gemini 1.5. Students found the steps were clear and easy to follow, offering useful demonstrations of the model's capabilities through relevant examples and helpful code snippets. Many appreciated how the lab directly shows how to call the API and implement features like image understanding and visual question answering, finding it excellent and concise for getting straight to the point. While the experience was overwhelmingly positive, a few learners felt it lacks depth for those with prior LLM experience and suggested concepts could be explained more thoroughly before the coding sections. Overall, it is seen as a great lab for gaining confidence in using Gemini 1.5.

Great for beginners, basic for pros.

"Might be too simple if you already have some experience with LLMs. Good for absolute beginners, I guess."

"A good introduction to multimodal Gemini 1.5."

"Felt a bit rushed."

"Good lab, but the concepts could be explained a bit more thoroughly before jumping into the code."

Technical environment worked well.

"The lab environment worked smoothly."

"I completed it quickly and feel more confident using the model. The lab environment worked smoothly."

"Had a minor issue with one command, but figured it out."

Demonstrates useful AI capabilities.

"Learned how to use Gemini 1.5 Pro for image understanding and text analysis. The steps were clear..."

"The lab covered interesting use cases."

"The examples were relevant."

"Very useful lab for understanding Gemini 1.5 capabilities."

Lab steps are easy to follow.

"The instructions were mostly clear."

"Clear steps, great examples."

"The steps were clear and easy to follow in the Google Cloud console."

"I found the steps to be very clear and easy to work through."

Effective for learning through doing.

"Great lab! Very practical and hands-on. Learned how to use Gemini 1.5 Pro for image understanding and text analysis."

"Excellent, concise lab. Directly shows how to call the Gemini 1.5 API for multimodal tasks..."

"Solid hands-on lab. Covered the main multimodal features of Gemini 1.5 Flash."

"The practical part is strong."

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 Use Cases with Gemini 1.5 with these activities:

Review Foundational Concepts of Large Language Models

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Reviewing the fundamentals of LLMs will provide a solid base for understanding Gemini 1.5's capabilities and multimodal applications.

Browse courses on Large Language Models

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Read articles on LLM architecture and training.
Watch introductory videos on NLP concepts.
Complete a basic online course on machine learning fundamentals.

Read 'Attention is All You Need'

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Reading the original Transformer paper will provide a deep understanding of the architecture underlying Gemini 1.5.

View Putting Knowledge to Work on Amazon

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Download and read the 'Attention is All You Need' paper.
Take notes on key concepts like self-attention and multi-head attention.
Research the Transformer architecture.

Follow Google Cloud's Gemini API Tutorials

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Following tutorials will provide hands-on experience with the Gemini API and its multimodal capabilities.

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Find the official Google Cloud documentation for the Gemini API.
Work through the tutorials on image and video processing.
Experiment with different prompts and parameters.

Four other activities

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Read 'Generative AI with Python and TensorFlow'

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Reading this book will provide a broader understanding of generative AI techniques.

View Generative AI with Python and TensorFlow 2:... on Amazon

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Obtain a copy of 'Generative AI with Python and TensorFlow'.
Read the chapters on image and text generation.
Experiment with the code examples provided in the book.

Build a Multimodal Chatbot with Gemini 1.5

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Starting a project will allow you to apply your knowledge of Gemini 1.5 to a real-world problem.

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Design the chatbot's functionality and user interface.
Implement the chatbot using the Gemini API.
Test and refine the chatbot's performance.

Write a Blog Post on Gemini 1.5 Use Cases

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Creating content will help you solidify your understanding of Gemini 1.5 and its potential applications.

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Research different use cases for Gemini 1.5.
Write a blog post that explains these use cases in detail.
Publish the blog post on a platform like Medium or your personal website.

Create a Presentation on Gemini 1.5 for a Technical Audience

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Creating a presentation will force you to synthesize your knowledge and communicate it effectively.

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Research the technical details of Gemini 1.5.
Create a slide deck that explains the model's architecture and capabilities.
Practice delivering the presentation to a technical audience.

Career center

Learners who complete Multimodal Use Cases with Gemini 1.5 will develop knowledge and skills that may be useful to these careers:

Natural Language Processing Engineer

A natural language processing engineer builds systems that allow computers to understand, interpret, and generate human language. This course will be useful for a natural language processing engineer since it teaches practical skills working with large language models. The course focuses on Gemini 1.5 Pro and Gemini 1.5 Flash, which are powerful tools for natural language tasks. A natural language processing engineer will be often working with multimodal applications, an area also explored by this course. The hands-on experience in the Google Cloud console, provided by this course, can be quite beneficial to your work as a natural language processing engineer.

See salaries and explore the career path for Natural Language Processing Engineer

Machine Learning Engineer

A machine learning engineer develops and implements machine learning models. This course will help you explore multimodal applications of large language models, which are becoming increasingly important in the field of machine learning. You'll learn to use Gemini 1.5 Pro and Gemini 1.5 Flash within the Google Cloud console, providing hands-on experience very relevant to building and deploying machine learning models. This course helps you understand how to integrate cutting-edge models into real-world applications. A machine learning engineer should have familiarity with cloud-based environments, such as Google Cloud, which is where this lab takes place. This will familiarize you with a popular platform for training and deploying large language models.

See salaries and explore the career path for Machine Learning Engineer

Artificial Intelligence Specialist

An artificial intelligence specialist focuses on the design, development, and implementation of artificial intelligence systems. This course may be useful for an artificial intelligence specialist because it offers experience with advanced large language models like Gemini 1.5 Pro and Gemini 1.5 Flash. The course provides practical experience with multimodal use cases, an area of increasing importance in AI. As an artificial intelligence specialist, you might work on various AI projects, and the knowledge gained using Google Cloud to deploy models in this course will help you contribute to the deployment of AI solutions. Hands-on experience with these tools, provided by this course, can significantly enhance one's capability to work with AI models.

See salaries and explore the career path for Artificial Intelligence Specialist

Data Scientist

A data scientist analyzes complex data to extract insights and create data-driven solutions. This course may be useful to a data scientist, as it provides hands-on experience with multimodal models using large language models. The data scientist often works with cutting-edge technologies, and this course explores how to use Gemini 1.5 Pro and Gemini 1.5 Flash in practical situations. In addition, data scientists frequently use cloud platforms, so the course’s focus on the Google Cloud console offers valuable familiarity with a popular platform where large language models are deployed. Working within this environment can help data scientists gain the kind of expertise crucial in their field.

See salaries and explore the career path for Data Scientist

Technical Consultant

A technical consultant advises clients on how to best use technology to solve business problems. This course will help you understand practical applications of advanced large language models like Gemini 1.5 Pro and Gemini 1.5 Flash. You will learn working in the Google Cloud console, a popular environment for deploying these models. A technical consultant should be familiar with modern cloud technologies, and this course will help you advise clients on how they can implement solutions based on AI. The course may be useful for advising clients on multimodal applications.

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Cloud Solutions Architect

A cloud solutions architect designs and oversees the implementation of cloud computing strategies. This course will be useful for a cloud solutions architect as it provides practical experience within the Google Cloud console, where you will explore the capabilities of Gemini 1.5 Pro and Gemini 1.5 Flash. This course will help you learn how to use cloud-based large language models. Cloud solutions architects should have detailed understanding of cloud infrastructure and services, and this course may provide hands-on experience that will be beneficial. You will learn to work with these cutting-edge models as part of architecting cloud based solutions.

See salaries and explore the career path for Cloud Solutions Architect

Computational Linguist

A computational linguist develops computational models of human language. This course may be useful for you as a computational linguist wanting practical applications of large language models. You will gain experience working in the Google Cloud console with tools like Gemini 1.5 Pro and Gemini 1.5 Flash. While this course does not focus on theory, working in a practical setting will help you make a bridge between theory and practice. Working with multimodal use cases may be relevant to working with human language and communication. This course offers a useful hands-on experience.

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Backend Developer

A backend developer focuses on the server-side logic, databases, and APIs that power applications. This course will help a backend developer who wants to build AI-powered applications. You will gain hands-on experience with Gemini 1.5 Pro and Gemini 1.5 Flash in the Google Cloud console. This course will provide valuable experience integrating these models into applications. Modern applications often require AI capabilities, so a backend developer will find familiarity with these technologies useful. You will also gain practical experience working in a cloud environment.

See salaries and explore the career path for Backend Developer

Software Developer

A software developer designs, develops, and tests software applications. This course may be useful to a software developer, especially if their work involves integrating AI and machine learning capabilities. The course focuses on using Gemini 1.5 Pro and Gemini 1.5 Flash, which can be integrated into various software applications. The lab takes place in the Google Cloud console, giving software developers insight into cloud-based model deployment and management. A software developer can use the knowledge from this course to expand their repertoire of technical capabilities.

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Data Engineer

A data engineer is responsible for building and maintaining the infrastructure that allows data to be used effectively. This course may be useful to a data engineer since it provides experience working on the Google Cloud platform, a popular platform for data processing and storage. While this course does not directly involve data infrastructure, you can gain experience working with Gemini 1.5 Pro and Gemini 1.5 Flash. A data engineer may be involved in making large language models accessible and useable within a large organization. Familiarity with a key cloud environment is helpful.

See salaries and explore the career path for Data Engineer

AI Product Manager

An AI product manager is responsible for the strategy, roadmap, and execution of AI-powered products. This course may be useful to an AI product manager as it will give you an understanding of how multimodal large language models function and their capabilities. This course focuses on the practical use of Gemini 1.5 Pro and Gemini 1.5 Flash on the Google Cloud platform. It can help you understand the technical considerations behind these models, and how they can be used in products. You will be able to make more informed decisions when bringing AI products to market.

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AI Research Scientist

An AI research scientist conducts research to advance the field of artificial intelligence. This course may be useful for an AI research scientist as it will help you explore the practical applications of multimodal large language models such as Gemini 1.5 Pro and Gemini 1.5 Flash. While an AI research scientist typically focuses on developing new algorithms and techniques, this course will help you understand real-world applications and challenges. As you will be working on the Google Cloud platform it is beneficial to know how these models are implemented in a popular cloud environment. Though research often involves creating models from scratch, an AI research scientist should also have knowledge of applied AI.

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Computer Vision Engineer

A computer vision engineer specializes in creating systems that allow computers to 'see' and interpret images and videos. This course may be useful for a computer vision engineer since it deals with multimodal models. While the course doesn't focus specifically on computer vision, an understanding of multimodal inputs is helpful for engineers who need to process and integrate data from both text and images. The lab setting will help you gain a familiarity with the Google Cloud platform and working with large language models. Computer vision engineers can leverage large language models to enhance their work.

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Robotics Engineer

A robotics engineer designs, builds, and tests robots and robotic systems. This course may be useful for a robotics engineer, especially if they work on robots that need to process and understand various types of information. This course will help you understand how large language models can be used in multimodal settings. While this course is not specifically about robotics, understanding how multimodal models function and how they can be deployed on Google Cloud can help you expand the capabilities of robotic systems. A robotics engineer should have knowledge of cloud-based systems.

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Business Intelligence Analyst

A business intelligence analyst is responsible for analyzing data to provide insights that can drive business decisions. This course may be useful to a business intelligence analyst who is interested in using AI tools to assist their work. While this course does not directly apply to data analysis, it can help you familiarize yourself with powerful new tools that are available on the Google Cloud Platform. This course focuses on multimodal capabilities of large language models, which will help you understand the opportunities these technologies present in a business setting. You will also gain familiarity with Google Cloud.

See salaries and explore the career path for Business Intelligence Analyst

Reading list

We've selected two books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Multimodal Use Cases with Gemini 1.5.

Putting Knowledge to Work

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This seminal paper introduces the Transformer architecture, which is the foundation for Gemini 1.5 and many other modern LLMs. Understanding the concepts of self-attention and encoder-decoder structures is crucial for grasping how these models process information. This paper provides the necessary background for understanding the architecture of Gemini 1.5. It must-read for anyone working with LLMs.

Putting Knowledge to Work

Paperback

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Putting Knowledge to Work

Kindle Edition

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Generative AI with Python and TensorFlow 2

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Provides a practical guide to generative AI techniques using Python and TensorFlow. While not specific to Gemini 1.5, it covers fundamental concepts and implementations relevant to understanding how multimodal models are trained and used. It is helpful for understanding the broader context of generative AI. This book is more valuable as additional reading than as a current reference.

Generative AI with Python and TensorFlow 2: Create...

Paperback

Generative AI with Python and TensorFlow 2: Create...

Kindle Edition

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