Google Vertex AI Tutorial & Overview (Better Than ChatGPT?!)
TLDRDiscover Google's Vertex AI, a powerful large language model rivaling ChatGPT. Explore its capabilities for building, deploying, and scaling machine learning models with fully managed tools. Dive into Model Gardens for standard models and fine-tunable models, and experiment with the generative AI Studio for customizing AI responses. Learn how to integrate Vertex AI with your applications through APIs, and join the Patreon community for exclusive access to apps and content.
Takeaways
- 🌟 Google's Vertex AI is a powerful platform for building, deploying, and scaling machine learning models with fully managed ML tools.
- 🛠️ The platform offers a wide range of models through its Model Garden, including foundation models like Palm 2 and fine-tunable models for various applications.
- 🎨 The Generative AI Studio within Vertex AI allows users to test, customize, and deploy generative AI models for different use cases.
- 🗣️ Users can interact with language models to generate content, summaries, and engage in conversations with specified formatting and style.
- 🔄 The platform provides examples and allows users to input their own prompts to tailor the AI's responses to their preferences.
- 🔧 Vertex AI also enables users to fine-tune existing models by uploading data that guides the model's behavior according to specific use cases.
- 📊 Model Garden includes a variety of models for text, image, audio, and video applications, catering to diverse needs.
- 🔗 The generative AI models can be integrated into other applications via APIs, allowing for seamless connectivity and functionality.
- 🔧 Users can experiment with different parameters such as temperature and token limits to control the creativity and length of the AI's responses.
- 📚 The tutorial also covers the process of connecting Vertex AI models to no-code platforms like Flutter Flow for easy app development and integration.
- 🎯 The video highlights the potential of Vertex AI as a strong alternative to other popular language models in terms of speed, customization, and integration capabilities.
Q & A
What is the main topic of the video?
-The main topic of the video is an introduction to Google's Vertex AI, a large language model (LLM), and its capabilities, including the Generative AI Studio and Model Garden.
How can one access the resources and apps demonstrated in the video?
-The resources and apps demonstrated in the video can be accessed through the Patreon community mentioned in the video description, where more information is provided using a link.
What is the purpose of the Model Garden in Vertex AI?
-The Model Garden in Vertex AI provides a variety of standard models that users can interact with for different applications, including text, image, audio, and video processing.
What feature of Vertex AI allows users to customize the behavior of the LLM?
-The Generative AI Studio feature of Vertex AI allows users to quickly test, tune, and customize generative AI models to suit their specific needs and applications.
How does the video demonstrate the use of prompts in the Generative AI Studio?
-The video demonstrates the use of prompts by showing how to design and test custom prompts, provide examples for the model to follow, and adjust the model's responses to get the desired format and style of answers.
What are some of the applications of the models available in Vertex AI?
-The models in Vertex AI can be applied to various tasks such as text generation, chat summarization, image classification, object detection, zone detection, audio processing, and video analysis.
How can users integrate Vertex AI models into their own applications?
-Users can integrate Vertex AI models into their applications by utilizing the provided API, which allows them to connect to the models and communicate with them from their own apps using methods like curl or programming languages like Python.
What is the process for tuning an existing model in Vertex AI?
-The process for tuning an existing model involves uploading a JSON file containing input-text and output-text pairs that define the desired behavior of the model. Users can then adjust the model's parameters and wait for the model to finish tuning.
How does the video illustrate the speed and responsiveness of Vertex AI compared to other models like ChatGPT?
-The video illustrates the speed and responsiveness of Vertex AI by showing how quickly it provides accurate and formatted responses to queries, suggesting that it may be faster and more precise than other models like ChatGPT.
What additional features and tools does Vertex AI offer for generative AI development?
-Vertex AI offers additional features and tools such as text-to-speech, speech-to-text, a feature store, datasets, labeling, training experiments, and a metadata model registry for generative AI development.
What benefits does the Patreon community offer for members interested in generative AI?
-Patreon community members get exclusive access to apps built on the channel, live streams, behind-the-scenes content, and a masterclass series voted on by the community, all of which support the development of generative AI applications.
Outlines
🌐 Introduction to Google's Large Language Model (LLM)
The paragraph introduces the viewer to the concept that there are more language models beyond GPT and ChatGPT. It sets the stage for a video that will showcase an amazing LLM from Google. The speaker mentions the availability of resources and apps through their Patreon community and provides a brief introduction to the app LM Studio, which allows for testing and interaction with various LLMs. The focus then shifts to explaining how to access and explore Google's Vertex AI, highlighting its capabilities for building, deploying, and scaling machine learning models with fully managed ML tools for any use case.
🔍 Exploring Vertex AI's Features and Generative AI Studio
This paragraph delves into the specifics of Vertex AI's dashboard and its various features. The speaker guides the viewer through navigating the console, accessing the Model Garden, and understanding the different types of models available, such as foundation models, fine-tunable models, and specialized models for text, audio, images, and videos. The Generative AI Studio is introduced as a key tool for quickly testing and customizing generative AI models, with a focus on language and speech models. The paragraph emphasizes the ability to design prompts, provide examples, and tune models to achieve desired responses and outcomes.
📝 Customizing Responses and Prompt Design
The speaker demonstrates how to customize the AI's responses through prompt design. By providing context and examples, the viewer learns how to shape the AI's answers to specific requirements, such as responding in short sentences or appending a word like 'sir' to the end of each answer. The paragraph showcases the process of giving the AI concrete examples to train it to behave in a certain way, and how to adjust parameters like temperature and token limits to control the creativity and length of the AI's responses.
🤖 Tuning Models and Extracting Information
This section focuses on the process of tuning existing models to better fit specific use cases. The speaker explains how to upload a JSON file containing input and output pairs to train the model according to the provided examples. The paragraph also discusses the various functionalities within the Generative AI Studio, such as classification, entity extraction, and ideation. Practical examples are given, such as summarizing news articles, extracting governing law from contracts, and generating ad copies for different marketing strategies.
🔗 Integrating AI Models with APIs and No-Code Apps
The speaker transitions into discussing the integration of AI models with other applications, emphasizing the importance of APIs for this purpose. The process of connecting to an AI model using an API is outlined, including the steps for activating a Cloud shell, logging into the Google account, and obtaining an access token. The paragraph then moves on to demonstrate how to build a no-code app using Flutter Flow that can connect to and receive responses from the AI model. The app is configured to send requests and display the AI's responses in a user-friendly format.
🚀 Conclusion and Encouragement for Generative AI Exploration
In the concluding paragraph, the speaker wraps up the video by showcasing the effectiveness of the AI model in providing accurate and formatted responses. The benefits of using Google's Vertex AI for building generative AI applications are highlighted, and the viewer is encouraged to explore the platform further. The speaker also promotes their Patreon community, where exclusive access to apps, live streams, and masterclass series is offered to members. The call to action is to join the Patreon community to support the channel and gain access to more content and resources related to generative AI.
Mindmap
Keywords
💡Google Vertex AI
💡Large Language Models (LLMs)
💡Model Garden
💡Generative AI Studio
💡Prompts
💡Model Tuning
💡API Integration
💡No-Code
💡Flutter Flow
💡Patreon Community
Highlights
Google's Vertex AI offers a powerful large language model (LLM) for various applications.
Vertex AI allows for the building, deploying, and scaling of machine learning models with fully managed ML tools.
The Model Garden provides access to standard models for interaction, including Foundation models like Palm 2 and fine-tunable models for specific use cases.
Generative AI Studio is a tool within Vertex AI that enables quick testing and customization of generative AI models.
Users can design and test their own prompts, allowing for a tailored response format from the AI.
The platform supports a variety of models for text, speech, images, and videos, catering to different applications.
Generative AI Studio provides examples and allows users to adjust model parameters such as temperature and token limits for desired creativity and response length.
Users can save their prompt designs and return to them later for further refinement.
Vertex AI also offers model tuning, where users can upload data to train the model according to their specific needs.
The platform includes capabilities for text classification, entity extraction, and content summarization.
Integration with external applications is possible through Vertex AI's API, enabling the use of the AI models within custom app environments.
An example of API integration is demonstrated with a no-code Flutter Flow app, showing the practical application of Vertex AI's generative models.
The video provides a walkthrough of connecting to Vertex AI's models using an API, including obtaining an access token and setting up the necessary headers and body for requests.
The generative AI models can be fine-tuned to provide very specific and concise answers, as demonstrated with the capital cities and population queries.
The video highlights the potential of Vertex AI for developers and creators looking to integrate AI into their projects for enhanced functionality and user experience.
The Patreon community offers exclusive access to apps built on Vertex AI, along with additional resources and masterclass series for deeper learning.