# Explore and Evaluate Models using Model Garden - GSP1166

## **Overview**

Model Garden on Vertex AI provides a single place to search, discover, and interact with a wide variety of models from Google and Google partners. Model Garden is available on Vertex AI and can be accessed from the Google Cloud console.

This lab provides a use case for you to explore Model Garden and then use Vertex AI Studio to create and experiment with prompts.

### Objectives

In this lab, you explore the following:

* Model Garden on Vertex AI to find the appropriate model for your use case.
    
* Types of Vertex AI Model in the Model Garden.
    

### Use case

You work for a real estate firm as a marketing analyst. Your company is interested in using large language models (LLMs) to return brief text descriptions of homes they are interested in and mortgage information.

You have been tasked with creating prompts that will summarize text from very long home descriptions on your real estate site. The home descriptions are stored in a file in a Google Cloud Storage bucket.

Begin by using Model Garden to explore available pre-built models to save time, and implement a solution as quickly as possible.

## **Vertex AI**

Vertex AI is Google Cloud's unified artificial intelligence platform for managing machine learning and generative AI projects.

## **Model Garden**

Model Garden on Vertex AI is a collection of pre-trained machine learning models and tools that are designed to simplify the process of building and deploying machine learning models.

These models could be in a wide variety of model types and sizes. Model Garden offers first-party models such as multimodal models from Google across vision, dialog, code generation, and code completion; or a wide variety of enterprise-ready open source models.

Model Garden also provides a variety of tools to help you use these models, including:

* **Model cards**: Model cards provide detailed information about each model, including its accuracy, performance, and training data.
    
* **Prompt design**: Prompt design allows you to interact with a model via a simple UI and tune the model with your own data.
    

One of the models available through Model Garden is the Cloud Natural Language API. The Cloud Natural Language API lets you extract entities from text, perform sentiment and syntactic analysis, and classify text into categories.

## **Vertex AI Studio**

Vertex AI Studio is a Google Cloud console tool for rapidly prototyping and testing generative AI models. You can test sample prompts, design your own prompts, and customize foundation models to handle tasks that meet your application's needs. You can perform the following:

* Test models using prompt samples.
    
* Design and save your own prompts.
    
* Tune a foundation model.
    
* Convert between speech and text.
    

## **Setup and requirements**

### Before you click the Start Lab button

Read these instructions. Labs are timed and you cannot pause them. The timer, which starts when you click **Start Lab**, shows how long Google Cloud resources will be made available to you.

This hands-on lab lets you do the lab activities yourself in a real cloud environment, not in a simulation or demo environment. It does so by giving you new, temporary credentials that you use to sign in and access Google Cloud for the duration of the lab.

To complete this lab, you need:

* Access to a standard internet browser (Chrome browser recommended).
    

**Note:** Use an Incognito or private browser window to run this lab. This prevents any conflicts between your personal account and the Student account, which may cause extra charges incurred to your personal account.

* Time to complete the lab---remember, once you start, you cannot pause a lab.
    

**Note:** If you already have your own personal Google Cloud account or project, do not use it for this lab to avoid extra charges to your account.

### How to start your lab and sign in to the Google Cloud console

1. Click the **Start Lab** button. If you need to pay for the lab, a pop-up opens for you to select your payment method. On the left is the **Lab Details** panel with the following:
    
    * The **Open Google Cloud console** button
        
    * Time remaining
        
    * The temporary credentials that you must use for this lab
        
    * Other information, if needed, to step through this lab
        
2. Click **Open Google Cloud console** (or right-click and select **Open Link in Incognito Window** if you are running the Chrome browser).
    
    The lab spins up resources, and then opens another tab that shows the **Sign in** page.
    
    ***Tip:*** Arrange the tabs in separate windows, side-by-side.
    
    **Note:** If you see the **Choose an account** dialog, click **Use Another Account**.
    
3. If necessary, copy the **Username** below and paste it into the **Sign in** dialog.
    
    ```apache
    student-01-d8f6c9b5b8e9@qwiklabs.net
    ```
    
    You can also find the **Username** in the **Lab Details** panel.
    
4. Click **Next**.
    
5. Copy the **Password** below and paste it into the **Welcome** dialog.
    
    ```apache
    O8uVZPJMHj3q
    ```
    
    You can also find the **Password** in the **Lab Details** panel.
    
6. Click **Next**.
    
    **Important:** You must use the credentials the lab provides you. Do not use your Google Cloud account credentials.
    
    **Note:** Using your own Google Cloud account for this lab may incur extra charges.
    
7. Click through the subsequent pages:
    
    * Accept the terms and conditions.
        
    * Do not add recovery options or two-factor authentication (because this is a temporary account).
        
    * Do not sign up for free trials.
        

After a few moments, the Google Cloud console opens in this tab.

**Note:** To view a menu with a list of Google Cloud products and services, click the **Navigation menu** at the top-left.

![Navigation menu icon](https://cdn.qwiklabs.com/nUxFb6oRFr435O3t6V7WYJAjeDFcrFb16G9wHWp5BzU%3D align="left")

### Enable APIs

1. In the Google Cloud console, from the **Navigation menu** (), click **More products**, and then select **Vertex AI** from the **Artificial Intelligence** section.
    
2. From the Vertex AI dashboard, click **Enable all Recommended APIs**.
    

## **Task 1: Explore Model Garden**

To view the list of available Vertex AI and open source foundation, tunable, and task-specific models, go to Model Garden.

* In the Vertex AI Dashboard, in the **Tools** pane on the left, click **Model Garden**.
    

The model categories available in Model Garden are:

| **Category** | **Description** |
| --- | --- |
| Foundation models | Pre-trained multitask large models that can be tuned or customized for specific tasks using AI Studio, Vertex AI API, and the Vertex AI SDK for Python. |
| Fine-tunable models | Models that you can fine-tune using a custom notebook or pipeline. |
| Task-specific solutions | Most of these pre-built models are ready to use. Many can be customized using your own data. |

Model cards are listed on the **Vertex AI Model Garden** page. Explore a Model card from each category. For example, explore the Task Sentiment analysis model, which inspects the provided text and identifies the prevailing emotional opinion within the text. This would be helpful to analyze the sentiment of Google reviews your real estate firm receives to keep track of your customer's happiness.

### Models in the AI Studio

1. From the Vertex AI Dashboard, in the **Tools** pane on the left, click **Model Garden** to return to the Model Garden main page.
    
2. In the **Foundation Models** section click **Show All** and then click on the **PaLM 2 Text Bison** model card.
    

The details page provides an overview of the PaLM 2 for Text model, including a description of what it is, an introduction to potential use cases, and documentation for the model.

Notice the **Open in Vertex AI Studio** button, which opens the AI Studio Language interface where you can interact with and experiment with the model. AI Studio is a feature of Vertex AI. It makes writing and tuning prompts for text, chats, and code generation simple and intuitive.

3. Click **Open in Vertex AI Studio** to open PaLM2 for Text in the AI Studio Language interface.
    

You can now explore this model to see how it responds to prompts.

#### **Test completed task**

Click **Check my progress** to verify your performed task.

Explore model garden.

**Check my progress**

## **Task 2: Explore Model types**

Model Garden is a single place to discover and interact with foundation models and popular open source models. With all of different enterprise-ready models you could use, Model Garden allows you to choose the right model for your use case, ML expertise, and budget.

With Model Garden, you can use a variety of workflows, including:

* Using a model directly as an API.
    
* Tuning the model in the AI Studio.
    
* Using the model directly in a Jupyter notebook through Vertex AI Workbench.
    
* Helping you deploy model training pipelines.
    

In this lab, you will explore some of these workflows.

### Models in a Jupyter notebook

1. In the **Tools** pane on the left, click **Model Garden** to return to the Vertex AI **Model Garden** page.
    
2. At the bottom of the **Foundation Models** section click **Show All** to expand the full list of foundation models.
    

You can see quite a few model group types in the left pane, which allows you to filter for models that meet your specific needs. Display only those models related to *vision* and *detection*:

3. Under **Modalities** click **Vision**.
    
4. Under **Tasks** click **Detection**.
    

Notice that there are now only a few models listed in the Foundation models section. The Owl ViT model is a zero-shot, text-conditioned, object detection model that can query an image with one or multiple text queries.

5. Click on the **Owl ViT** model card.
    

Notice that the Vertex AI **OWL-ViT** page has an **Open Notebook** link to open a JupyterLab Notebook.

6. Click **Open Notebook** to open the Owl ViT Colab in a new tab.
    

Review the Colab notebook but you do not need to run it.

This Colab notebook demonstrates how to deploy the pre-trained OWL-ViT model on Vertex AI for online prediction.

To learn more about Colab notebooks, visit the homepage for [Google Colaboratory](https://colab.google/).

7. Close the Colab notebook tab to return to the Cloud Console tab.
    

For the models you want to fine-tune, Model Garden on Vertex AI provides you an easy way to get started.

### Models as part of model model training pipelines:

1. In the **Tools** pane on the left, click **Model Garden** to return to the **Vertex AI Model Garden** page.
    
2. Clear the filter selections under **Modalities** and **Tasks** if it is not cleared already.
    
3. Type "bert" in the **Search Models** search bar and select **BERT** model from the search list.
    
    **Note:** Depending on your browser width, you may have to click **Show all** or expand your window to view the **BERT** model card.
    
4. Click **Fine-Tune** to open the **bert-finetuning** Vertex AI pipeline.
    

Review the pipeline but you do not need to run it.

Click **Check my progress** to verify your performed task.

Explore model types.

**Check my progress**

This brings you to a template that you can use to fine tune and deploy this model. You can see the various components of this pipeline that this template would execute.

In your own production environment, you would click **Create Pipeline**, fill in or confirm the required information, and then click **Submit**. This deploys a pipeline without you ever having to write code.

---

## Solution of Lab

%[https://www.youtube.com/watch?v=PUzTHBZa1cw] 

```apache
curl -LO raw.githubusercontent.com/quiccklabs/Labs_solutions/master/Explore%20and%20Evaluate%20Models%20using%20Model%20Garden/quicklabgsp1166.sh
sudo chmod +x quicklabgsp1166.sh
./quicklabgsp1166.sh
```