Introduction to Vertex AI Model Garden

Google Cloud Tech
7 Jun 202305:59

TLDRVertex AI Model Garden is a platform on Google Cloud that offers a variety of enterprise-ready models for machine learning projects. It allows users to access, build, and manage models with ease, and supports different workflows such as direct API use, model tuning, and deployment. The video highlights the use of models like PaLM 2 for text and OWL Vision Transformer for computer vision tasks, showcasing how Model Garden simplifies the process of finding, using, and fine-tuning models with minimal coding.

Takeaways

  • 🚀 Vertex AI is a fully managed machine learning platform on Google Cloud, supporting various AI workloads like data science, MLOps, and AI app development.
  • 🌱 Model Garden on Vertex AI is a centralized place to discover and interact with Google's industry-leading models and popular open-source models, integrated with enterprise MLOps tooling.
  • 🔍 Model Garden allows users to choose the right model for their use case, ML expertise, and budget, offering a variety of enterprise-ready models to choose from.
  • 📈 Users can initiate different workflows with the models in Model Garden, such as using them directly via API, tuning in Generative AI Studio, or deploying model training pipelines.
  • 📖 The video script provides a walkthrough of how to use the PaLM 2 model for text through the Generative AI Studio and how to interact with other models via a Jupyter notebook.
  • 🤖 Generative AI Studio enables interaction with models through a simple UI or allows users to fine-tune models with their own data for better customization.
  • 🖼️ For computer vision tasks, Model Garden offers models like the OWL Vision Transformer, a zero-shot, text-conditioned object detection model that can query images with text.
  • 📚 The video demonstrates how to use Model Garden to find a suitable model, deploy it on Vertex AI, and get predictions, simplifying the process of integrating AI into applications.
  • 💡 Model Garden simplifies the fine-tuning process for models like BERT by providing a pipeline template that requires minimal or no coding, making it easier for users to customize models with their data.
  • 🔗 The video encourages viewers to explore Model Garden further by providing a link for more information on how to utilize this resource effectively.

Q & A

  • What is Vertex AI Model Garden?

    -Vertex AI Model Garden is a single place on Vertex AI where you can discover and interact with Google's industry-leading models and popular open source models, supported by Google Cloud's enterprise MLOps tooling.

  • What kind of workloads does Vertex AI support?

    -Vertex AI supports various workloads such as traditional data science, machine learning, MLOps, and building AI-powered applications.

  • What are the two major tools recently announced for Vertex AI?

    -The two major tools recently announced for Vertex AI are Model Garden and Generative AI Studio.

  • How does Model Garden help developers?

    -Model Garden helps developers by providing pre-trained models ready to use out of the box, which can accelerate the time to getting value by allowing quick implementation of relevant models.

  • What can you do with the PaLM 2 model for text in Generative AI Studio?

    -With the PaLM 2 model for text in Generative AI Studio, you can interact with the model via a simple UI or tune the model with your own data, allowing for tasks like explaining complex concepts in simple terms.

  • How can data scientists use Model Garden to find models for their use cases?

    -Data scientists can filter for specific types of models in Model Garden, such as vision-related models, and explore various options like the OWL Vision Transformer for object detection tasks.

  • What is the benefit of using the Colab notebook for OWL Vision Transformer?

    -The Colab notebook for OWL Vision Transformer provides a guided walkthrough on how to understand and work with image files, including deploying the model on Vertex AI and sending images for predictions to get text captions describing the image content.

  • How does Model Garden facilitate fine-tuning of models like BERT?

    -Model Garden simplifies the fine-tuning process by wrapping it in a pipeline template, which requires minimal or zero coding, making it easier for users to fine-tune and deploy models with their own data.

  • What is the process for fine-tuning a model using Model Garden's pipeline?

    -To fine-tune a model, users can click on 'Open Fine-tuning Pipeline', select a pipeline template, enter required parameters like storage locations for outputs and input data, and submit the request to start the pipeline.

  • How does Vertex AI Model Garden make model interaction easy?

    -Vertex AI Model Garden streamlines the process of finding, deploying, and using models by providing a curated selection of enterprise-ready models, simple UI interactions, notebook guides, and pipeline templates for fine-tuning.

  • What additional resources are available for learning more about Vertex AI Model Garden?

    -For more information on Vertex AI Model Garden, users can check out the link provided below the video transcript, which likely leads to further documentation or tutorials.

Outlines

00:00

🚀 Introduction to Vertex AI Model Garden

This paragraph introduces the viewer to Vertex AI Model Garden, a platform designed to jump-start ML projects. It highlights the capabilities of Vertex AI, a fully managed ML platform on Google Cloud, which supports various workloads such as traditional data science, machine learning, MLOps, and AI-powered app development. The video aims to provide insights into Model Garden, a tool that allows users to discover and interact with Google's industry-leading models and popular open-source models, all supported by Google Cloud's enterprise MLOps tooling. It emphasizes the ease of choosing the right model based on use case, ML expertise, and budget, and outlines the various workflows that can be initiated through Model Garden, including using models directly, tuning in Generative AI Studio, using in a Jupyter notebook, and deploying model training pipelines. The paragraph also discusses the benefits for developers and data scientists in finding pre-trained models and the process of exploring and using these models, such as the PaLM 2 for text and OWL Vision Transformer for computer vision tasks.

05:02

🛠️ Utilizing Model Garden for Model Fine-Tuning and Deployment

This paragraph delves into the process of fine-tuning models and deploying them using Vertex AI Model Garden. It describes how Model Garden simplifies the fine-tuning process by providing a pipeline template that can be executed with minimal or no coding. The user is guided through the steps of using the pipeline, including creating a run, inputting parameters for data storage and training data location, and submitting the pipeline for execution. The paragraph emphasizes the ease of use and efficiency of Model Garden, which allows users to quickly utilize and deploy models without the need for extensive coding knowledge. It concludes by encouraging viewers to explore Vertex AI Model Garden for a wide variety of curated and enterprise-ready models and provides a link for more information.

Mindmap

Keywords

💡Vertex AI

Vertex AI is a fully managed machine learning (ML) platform provided by Google Cloud. It offers a suite of services that enable users to access, build, experiment, deploy, and manage models. In the context of the video, Vertex AI is the foundation upon which Model Garden and other tools are built, allowing users to efficiently work on their ML projects and AI applications.

💡Model Garden

Model Garden is a feature within Vertex AI that serves as a centralized space for discovering and interacting with a variety of industry-leading models and popular open-source models. It is integrated with Google Cloud's enterprise MLOps tooling, providing users with enterprise-level support. Model Garden allows users to select the appropriate model based on their specific use case, ML expertise, and budget, streamlining the process of initiating ML workflows.

💡Generative AI Studio

Generative AI Studio is a tool introduced alongside Model Garden as part of Vertex AI's suite of services. It enables users to interact with models through a simple user interface or fine-tune models using their own data. In the video, it is showcased as a way to prototype with models like the PaLM 2 for text, providing a user-friendly environment for model experimentation and customization.

💡PaLM 2

PaLM 2, or Pathways Language Model 2, is a large language model developed by Google that is designed to handle a variety of natural language processing tasks. In the video, it is demonstrated as an example of a model that can be used directly through the Generative AI Studio, where users can ask questions and receive explanations, such as explaining large language models in a simplified manner.

💡Jupyter Notebook

A Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations, and narrative text. In the context of the video, Jupyter Notebooks are accessed through Vertex AI Workbench, enabling users to interact with models like the OWL Vision Transformer for image analysis, providing a flexible and interactive environment for data scientists to explore and utilize models.

💡MLOps

MLOps, a portmanteau of Machine Learning and Operations, refers to the set of practices for managing the end-to-end lifecycle of machine learning models. In the video, it is mentioned as one of the workloads supported by Vertex AI, emphasizing the platform's capability to not only build models but also to deploy and manage them in a scalable and reliable manner within an enterprise setting.

💡OWL Vision Transformer

The OWL Vision Transformer is an open-source, zero-shot text-conditioned object detection model featured in the video. It is capable of querying an image with multiple text queries and providing a text caption describing the content of the image. This model is showcased as an example of the enterprise-ready models available in Model Garden, which can be easily deployed and used through a Colab notebook, illustrating the ease of using models within Vertex AI.

💡Fine-tuning

Fine-tuning is the process of adapting a pre-trained machine learning model to a specific task or dataset. In the video, it is mentioned as a capability of Model Garden, allowing users to fine-tune models like BERT with their own data through a pipeline template. This simplifies the process by reducing the need for extensive coding and technical expertise, making it accessible for users to improve model performance for their particular use cases.

💡Pipeline

In the context of the video, a pipeline refers to a sequence of steps or processes that are automated to perform specific tasks, such as model training or fine-tuning. Model Garden provides pipeline templates that users can execute with minimal or no coding, as demonstrated when fine-tuning the BERT model. This pipeline approach streamlines the model development process, making it more efficient and user-friendly.

💡BERT

BERT, which stands for Bidirectional Encoder Representations from Transformers, is a pre-trained language representation model developed by Google. It is used for a wide range of natural language processing tasks. In the video, BERT is mentioned as an example of a model that can be fine-tuned through Model Garden's pipeline, allowing users to tailor the model to their specific needs by supplying their own data, without the need for extensive coding.

Highlights

Vertex AI Model Garden is a platform to jump-start ML projects.

It allows access to Google's industry-leading models and popular open source models.

Model Garden integrates with Google Cloud's enterprise MLOps tooling.

Users can choose models based on their use case, ML expertise, and budget.

The platform supports various workflows, including using models directly as APIs.

Generative AI Studio allows model interaction and tuning with custom data.

Developers can utilize pre-trained models ready for immediate use.

PaLM 2 model for text offers interactive explanations and examples.

Data scientists can find models for specific use cases like text and images.

OWL Vision Transformer is a zero-shot, text-conditioned object detection model.

Model Garden simplifies deployment and use of models with minimal coding.

BERT and similar models can be fine-tuned with user data using Model Garden.

Fine-tuning pipelines are wrapped and easy to use with minimal or zero coding.

Creating a run in the pipeline template requires minimal parameters and initiates the model training process.

Model Garden makes the process of finding, deploying, and using models super easy.

It enables the discovery and interaction with a variety of curated and enterprise-ready models.