Introduction to Gemini on Vertex AI

Summary

In this tutorial, Holt Skinner introduces developers to Google Cloud's Gemini models within Vertex AI. He demonstrates how to leverage these multimodal models for scalable AI applications like chatbots, code generation, and document processing. Focusing on Gemini 1.5 Pro and Flash, the video explores their ability to handle vast data sets with long context windows. Developers are guided through using Vertex AI Studio, customizing prompts, and integrating code for quick API calls. Additional resources like Jupyter Notebooks and GitHub repositories are shared to help developers dive deeper into generative AI, providing everything needed to start building AI-powered applications.

Takeaways

• 😀 **Gemini Models in Vertex AI**: Vertex AI on Google Cloud allows developers to integrate Gemini models, which are powerful generative AI tools for applications like chatbots, image recognition, and video processing.
• 😀 **Target Audience**: The video is aimed at developers looking to build scalable, enterprise-grade applications using generative AI or large language models (LLMs).
• 😀 **Google’s Gemini Product Ecosystem**: Gemini models are available for various use cases: Gemini Web App for consumers, Gemini for Google Workspace, Gemini for Google Cloud, and the Gemini API for Developers.
• 😀 **Vertex AI Overview**: Vertex AI is a managed platform for AI applications on Google Cloud, offering features like custom training, prediction pipelines, fine-tuning, and API access for generative AI models.
• 😀 **Gemini Models**: Gemini models are multimodal, capable of understanding text, images, code, documents, audio, and video. They are designed using transformer and mixture-of-experts neural network architectures.
• 😀 **Gemini 1.5 Versions**: The latest Gemini version, 1.5, comes in two models: Gemini 1.5 Pro (general-purpose, high-performance) and Gemini 1.5 Flash (optimized for speed and efficiency).
• 😀 **Large Context Windows**: Gemini 1.5 models support long context windows—up to 2 million tokens for Pro and 1 million tokens for Flash—allowing them to process massive data like hours of video or thousands of lines of code.
• 😀 **Vertex AI Studio**: The platform provides an interactive environment to explore and customize Gemini models. Developers can interact with models in Freeform or Chat Mode and explore prewritten prompts in the Prompt Gallery.
• 😀 **Example Use Case**: A practical demonstration shows how to use Gemini to summarize a news article, and how users can refine the prompt to adjust the tone or style of the output.
• 😀 **Model Customization and Code Generation**: Users can save refined prompts for future use and quickly generate code in multiple languages through the Vertex AI Studio interface, making the transition from prompt to API call seamless.
• 😀 **Further Learning Resources**: Developers are encouraged to explore the linked Jupyter Notebook for a detailed guide and check out the Google Cloud Generative AI GitHub Repository for more examples, demos, and to contribute their own use cases.

Q & A

What is the purpose of this video by Holt Skinner?

The video aims to teach developers how to use Gemini models in Vertex AI on Google Cloud, focusing on building scalable, enterprise-grade applications that leverage generative AI models for tasks like chatbots, code generation, document understanding, and more.

What are the primary use cases for Gemini models mentioned in the video?

The Gemini models are used for various tasks, including chatbots, agents, image recognition, document understanding, text and code generation, and video processing.

How does Google categorize its Gemini AI offerings for different audiences?

Google offers several Gemini-related products: Gemini web app (for consumers), Gemini for Google Workspace (for streamlining workflows), Gemini for Google Cloud (for general cloud tasks and code generation), and the Gemini API (for independent developers or small teams).

What is Vertex AI, and why is it significant for developers?

Vertex AI is a managed, end-to-end AI platform by Google Cloud that enables developers to create custom AI models, handle training and prediction pipelines, fine-tune models, use Jupyter Notebooks, and access various generative AI models from Google and other sources.

What makes the Gemini 1.5 model so powerful?

Gemini 1.5 is a multimodal model that can understand and process text, images, audio, video, code, and documents. It supports long context windows (up to 2 million tokens for Gemini 1.5 Pro), allowing it to handle large amounts of data, like 2 hours of video or 1.4 million words.

What are the differences between Gemini 1.5 Pro and Gemini 1.5 Flash?

Gemini 1.5 Pro is designed for general high performance across a range of tasks, while Gemini 1.5 Flash is optimized for speed and efficiency, making it lighter and faster at the cost of some performance.

How does the context window in Gemini models benefit users?

The long context windows in Gemini models allow them to process large amounts of data in a single interaction. For example, the Pro version can handle up to 2 million tokens, enabling it to work with long documents, videos, or extensive codebases without losing context.

What is Vertex AI Studio, and how is it used in the video?

Vertex AI Studio is a user interface within Google Cloud's Vertex AI platform that allows developers to quickly explore and customize generative AI models. It includes features like Chat Mode for conversation history and Freeform Mode for general use, along with a prompt gallery for various tasks.

What is the Prompt Gallery in Vertex AI Studio, and how can it help developers?

The Prompt Gallery in Vertex AI Studio is a collection of prewritten prompts designed to demonstrate Gemini's capabilities. Developers can filter these prompts by data types, tasks, and features, making it easier to find relevant examples and use cases.

How can developers refine their prompts to get better results from Gemini models?

Developers can refine their prompts using a 'magic wand' icon in Vertex AI Studio. By providing specific instructions on how they want the model to respond, they can adjust the model's output to match their preferred style or tone, as shown in the example of refining a news article summary.