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Introduction to Vertex AI Studio

Science & Technology

Introduction to Vertex AI Studio

Welcome to the Vertex AI Studio course! Vertex AI Studio is the primary tool for cloud developers to access Google's cutting-edge generative AI (GenAI) models. It facilitates the testing, tuning, augmenting, and deployment of these models, enabling the creation of GenAI-powered applications. This article provides an in-depth understanding of Vertex AI Studio, teaching you the knowhow to navigate it effectively. It starts by explaining the GenAI workflow and introduces major features of Vertex AI Studio, including Gemini Multimodal, prompt design, and model tuning. Additionally, a hands-on lab at the end provides an opportunity to practice the skills you have acquired.

What is Generative AI?

Generative AI is transforming how we interact with technology. Simply put, it is a type of artificial intelligence that generates content for you. The generated content can be multimodal, including text, images, audio, and video. When given a prompt or a request, GenAI can help you achieve various tasks such as document summarization, information extraction, code generation, marketing campaign creation, virtual assistance, and call center bots. Generative AI learns from a massive amount of existing content, including text, images, and video. This process of learning is called 'training,' and it results in the creation of foundation models.

Foundation Models

A foundation model is usually large in terms of the significant number of parameters, massive size of training data, and high computational power. LLM (Large Language Model) like PaLM (Pathways Language Model) is an example. Other foundation models trained by Google include Gemini for multimodal processing, Codey for code generation, and Imagen for image processing. Note that Gemini may replace some of these models as it is capable of processing data in multiple modalities. The pre-trained foundation model can generate content and solve general problems. It can also be further trained or tuned with new datasets in your field to solve specific problems, resulting in a new model tailored to your specific needs.

Vertex AI

Vertex AI is a comprehensive machine learning platform offered by Google Cloud. It supports end-to-end machine learning processes, including model creation, deployment, and management. Vertex AI provides two primary capabilities: predictive AI and generative AI. In generative AI, you can use and tune GenAI models to produce content.

Workflow in Vertex AI

  1. Input prompt: Enter a natural language prompt via the Vertex AI Studio UI.
  2. Responsible AI and Safety Measures: The prompt undergoes responsible AI checks.
  3. Foundation Models: The screened prompt proceeds to foundation models like Gemini Multimodal.
  4. Model Customization: Optionally customize GenAI models to fit your data and use cases.
  5. Results Grounding: The models return results that undergo grounding and citation checks.
  6. Final Response: The final response appears on the UI after the final check.

Using Gemini Multimodal

Gemini is capable of processing input from multiple modalities (text, images, video). It can identify objects in images, extract information, analyze the extracted information, answer questions, create stories, and convert data formats. It can be accessed through three main approaches:

  1. User Interface (UI): Ideal for exploring and testing prompts.
  2. Predefined SDKs: Integrated with notebooks like Colab and Workbench.
  3. APIs and Command-line tools: Using APIs in conjunction with tools like curl.

Components of a Prompt

  1. Input: The request for a response from the model.
  2. Context: Instructions to guide the model's behavior and provide information.
  3. Examples: Input-output pairs demonstrating the desired response format.

Prompt design often involves a lot of experimentation to produce the desired responses. Three methods commonly used are:

  1. Zero-shot prompting: The model is given a prompt that describes the task without additional examples.
  2. One-shot prompting: The model is given a single example of the task.
  3. Few-shot prompting: The model is given a small number of examples.

Model Tuning

After creating effective prompts, you can further refine the model using tuning techniques. The model can be tuned from less technical methods like prompt design to more technical methods like distillation.

Techniques in Model Tuning

  1. Prompt Design: Customizes the model's responses without altering its parameters.
  2. Parameter-Efficient Tuning: Modifies a subset of model parameters or adds layers.
  3. Distillation: Trains smaller, task-specific models with less training data.

Examples and Hands-On Practice

The Vertex AI Studio offers hands-on labs where you can:

  1. Analyze images with Gemini Multimodal.
  2. Explore Multimodal capabilities.
  3. Design prompts using both free form and structured modes.
  4. Generate conversations and improve specific tasks.

For a deeper understanding of language models and other transformative technologies, refer to the course titled "Natural Language Processing on Google Cloud."

We hope you enjoyed this article on Vertex AI Studio. Be sure to check out other Google Cloud courses for continued learning.

Keywords

  • Vertex AI Studio
  • Generative AI
  • Gemini Multimodal
  • LLM (Large Language Model)
  • PaLM (Pathways Language Model)
  • Codey
  • Imagen
  • Prompt Design
  • Model Tuning
  • Parameter-Efficient Tuning
  • Foundation Models
  • Google Cloud

FAQ

1. What is generative AI?

Generative AI is a type of artificial intelligence that generates content based on the massive amount of data it has been trained on. Content can include text, images, audio, and video.

2. What are foundation models?

Foundation models are large models trained on massive datasets. Examples include PaLM for language modeling, Gemini for multimodal processing, Codey for code generation, and Imagen for image processing.

3. How can I access GenAI models using Vertex AI?

You can use Vertex AI Studio's user interface, predefined SDKs integrated with notebooks, or APIs along with command-line tools.

4. What is a prompt in generative AI?

A prompt is a natural language request given to the model to generate a response. It includes input, context, and examples to guide the model's behavior.

5. What is the difference between zero-shot, one-shot, and few-shot prompting?

  • Zero-shot prompting: The model is given a task description without additional examples.
  • One-shot prompting: The model is given a single example of the task.
  • Few-shot prompting: The model is given a small number of examples to demonstrate the task.

6. What are some techniques for tuning generative AI models?

Techniques for tuning include prompt design, parameter-efficient tuning, reinforcement learning, and distillation.

7. How does distillation work?

Distillation trains smaller, task-specific models using labeled examples and rationales generated by a larger teacher model, optimizing performance, latency, and cost.