Google Vertex AI Tutorial & Overview (Better Than ChatGPT?!)
TLDRDiscover Google's Vertex AI, a powerful large language model rivaling ChatGPT. Explore its capabilities in model building, deployment, and scaling with managed ML tools. Dive into Model Gardens for standard and fine-tunable models, and the innovative Generative AI Studio for customizing AI responses. Learn how to integrate these models into applications via APIs, and join Patreon for exclusive access to apps and in-depth content.
Takeaways
- 🌐 Google Vertex AI is a platform for building, deploying, and scaling machine learning models with fully managed ML tools for various use cases.
- 🔍 The Model Garden within Vertex AI offers a variety of standard models for different applications, including text, image, audio, and video processing.
- 🎨 Generative AI Studio allows users to test, customize, and deploy generative AI models, leveraging their capabilities in applications with a focus on language and speech models.
- 📝 Users can design and test their own prompts for AI models, refining the model's responses based on specific instructions and examples.
- 🔄 Model tuning is possible by uploading data in JSONL format, which helps the model learn desired behaviors and responses.
- 📊 The platform provides examples and contexts for various use cases, such as chat summarization, classification, entity extraction, writing, and ideation.
- 🔗 API integration is available for the models, enabling easy connection and communication with the models from external applications.
- 🚀 No-code tools like Flutter Flow can be used to build applications that integrate with Vertex AI's models through API calls.
- 🌟 Patreon community members get exclusive access to apps built on the channel and can clone and experiment with generative AI apps using Vertex AI.
- 🎥 The video provides a detailed tutorial and overview of Google's Vertex AI, positioning it as a strong alternative to other popular language models like ChatGPT.
Q & A
What is Google Vertex AI?
-Google Vertex AI is a platform that enables users to build, deploy, and scale machine learning models faster with fully managed ML tools for any use case.
How can one access Google Vertex AI?
-To access Google Vertex AI, one needs to search for 'Google Vertex AI', go to the console, and if they have Firebase or Google projects, they can directly use it. Otherwise, they need to create a new project.
What types of models are available in Vertex AI's Model Garden?
-Model Garden offers a variety of models including foundation models like Palm 2 for text and chat, fine-tunable image classification models like Vision Transformer, and models for detecting people, vehicles, and more in videos or images.
What is the Generative AI Studio in Vertex AI?
-The Generative AI Studio is a tool within Vertex AI that allows users to quickly test and customize generative AI models to leverage their capabilities in applications.
How can users experiment with prompts in the Generative AI Studio?
-Users can design, test, and refine their own prompts by providing instructions and examples to the models. They can adjust the model's responses to fit their desired format and content.
What parameters can users adjust in the Generative AI Studio?
-Users can adjust parameters like temperature, which controls the creativity of the model, and token limits, which determine the maximum amount of text output from one prompt.
How can users save and revisit their prompt designs?
-Users can save their prompts by clicking on 'My Prompts' and revisit them later for further adjustments or testing.
What is model tuning in the context of Vertex AI?
-Model tuning in Vertex AI involves uploading a JSON file containing input-text and output-text pairs to train the model according to specific user requirements.
How can the AI models in Vertex AI be integrated into applications?
-The Generative AI Studio provides an API that allows users to connect the AI models to their applications. Users can use the API through various programming languages or tools like Flutter Flow.
What is the benefit of using Vertex AI for generative AI applications?
-Using Vertex AI for generative AI applications allows for faster and potentially more customized responses, as users can fine-tune models and adjust them to their specific needs. It also provides a range of models suitable for various applications.
Outlines
🌐 Introducing Google's Large Language Model (LLM) and Vertex AI
The paragraph introduces the viewer to Google's large language model (LLM) and Vertex AI, highlighting that there are numerous LLMs beyond GPT and ChatGPT. The speaker mentions an app called LM Studio that allows for testing and interaction with various LLMs. The focus then shifts to explaining how to access and utilize Vertex AI by searching for it and navigating through the platform's features, such as Model Garden and Generative AI Studio. The speaker emphasizes the ability to build, deploy, and scale machine learning models efficiently using Vertex AI's fully managed tools.
🛠️ Exploring Generative AI Studio and Prompt Design
This paragraph delves into the functionalities of the Generative AI Studio within Vertex AI, where users can test, customize, and leverage generative AI models for their applications. The speaker discusses the ability to experiment with different prompts, which are instructions given to the LLM to shape the output. The paragraph also covers the process of fine-tuning a model by providing it with examples to guide its responses. The speaker demonstrates how to use the Generative AI Studio to get desired answers from the model, adjusting parameters such as temperature and token limits to control the creativity and length of the output.
📝 Customizing Responses and Model Tuning
The speaker continues to explore the customization of AI responses through the use of examples and the model tuning process. The paragraph explains how users can shape the AI's answers by providing specific questions and corresponding desired answers. It also introduces the concept of 'tuning' an existing model by uploading a JSON file containing input-output pairs to alter the model's behavior according to user preferences. The speaker provides an example of how to format and use this file for model tuning, emphasizing the flexibility and adaptability of the AI to meet specific user needs.
🔗 Integrating AI Models with Applications via APIs
This paragraph discusses the integration of AI models with applications through APIs. The speaker explains the process of connecting to an AI model using an API, starting with executing a command in a cloud shell to obtain an access token. The paragraph details the steps to configure an API call within a no-code tool like Flutter Flow, including setting up the request with the necessary headers and body. The speaker demonstrates how to test the API call and extract the relevant information from the response to be used within an app. The focus is on the practical application of the AI model in a real-world scenario, showcasing the ease of integration and the potential for creating AI-powered applications.
🎉 Demonstrating AI Integration in a No-Code App
The speaker concludes the video by demonstrating the successful integration of the AI model into a simple no-code app built in Flutter Flow. The app allows users to input questions and receive answers from the AI model via API calls. The paragraph highlights the speed and accuracy of the AI's responses, which are tailored to the user's specifications through the examples and tuning process explained earlier. The speaker emphasizes the potential of Vertex AI for building generative AI applications and invites viewers to explore the platform further, while also promoting the Patreon community for exclusive access to the apps and content created by the speaker.
Mindmap
Keywords
💡Google Vertex AI
💡Large Language Models (LLMs)
💡Model Garden
💡Generative AI Studio
💡Prompts
💡Fine-tuning
💡API Integration
💡Flutter Flow
💡No-Code
💡Patreon Community
Highlights
Google's Vertex AI offers a powerful large language model (LLM) for various applications.
Vertex AI enables faster building, deploying, and scaling of machine learning models with fully managed ML tools.
Model Garden provides access to standard models like Foundation models and fine-tunable models for specific use cases.
Generative AI Studio allows users to test, customize, and leverage generative AI models for applications.
Language models in Vertex AI can be used for chat generation, content generation, chat summarization, and more.
The platform offers a variety of models for text, audio, image, and video applications.
Users can design and test their own prompts to get desired responses from the AI models.
Prompt tuning allows users to upload data to train and customize models according to their preferences.
API integration enables developers to connect Vertex AI models with their own applications for seamless functionality.
The tutorial demonstrates connecting Vertex AI with a no-code platform like Flutter Flow for easy app development.
The generative AI models can be fine-tuned with examples and specific formatting for precise outputs.
Vertex AI's generative models are noted for their speed and ability to respond in a format specified by the user.
The platform supports a wide range of languages for speech models, including text-to-speech and speech-to-text.
Exclusive apps and tools used in the video are available to members of the Patreon community.
The video provides a comprehensive overview of Vertex AI's capabilities and practical applications.
