Generative AI Studio and Google Vertex AI Solutions

In the world of generative AI, two solutions stand out: Generative AI Studio and Google Vertex AI. Generative AI Studio is a comprehensive platform that enables developers to build and deploy AI models.

Google Vertex AI, on the other hand, is a powerful tool that provides a range of AI services, including machine learning and deep learning capabilities. This platform is designed for large-scale AI development and deployment.

One key difference between the two solutions is their approach to AI model development. Generative AI Studio focuses on providing a user-friendly interface for developers to build and customize AI models, while Google Vertex AI takes a more comprehensive approach, offering a range of AI services and tools.

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Getting started with Generative AI Studio is a breeze. You can jump right in with one of the quickstarts.

Try the Gemini API to generate text, or use the SDK to send requests to the Gemini API in Vertex AI. This is a great way to get started with text generation.

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The Vertex AI Studio Prompt Gallery is another option, allowing you to send prompts to Gemini with no setup required. This is perfect for testing prompts without any hassle.

If you want to generate an image, you can use Imagen on Vertex AI to create a watermarked image. This is a great way to verify the authenticity of an image.

Here are the quickstarts in a concise list:

In a generative AI studio, you can access Google's models like Gemini, Imagen, Codey, and Chirp, which offer a wide range of capabilities.

You can easily tune foundation models with your own data, making them more accurate and effective for your specific needs. This is especially useful for tasks like image generation and text extraction.

The studio also features a user-friendly interface for designing multimodal prompts, which allow you to combine different types of input, such as natural language and images, to generate more complex and nuanced outputs.

Here are some key features of the studio:

Our product is designed to make working with generative AI more accessible and efficient. You can access Google's models like Gemini, Imagen, Codey, and Chirp with ease.

One of the key features is the ability to tune foundation models with your own data. This allows you to customize the models to fit your specific needs.

Some of the common uses of our product include enabling more people to build with generative AI. This can be a game-changer for businesses and individuals looking to leverage AI technology.

Here are some of the product highlights:

Gemini is a powerful multimodal model that lets you interact with natural language, code, or an image.

You can access Gemini and other models like Imagen, Codey, and Chirp through Google's interface.

To get started with Gemini, you can prompt and test it in Vertex AI using natural language, code, or an image. This means you can try out sample prompts for tasks like extracting text from images or generating answers about uploaded images.

Some common uses for Gemini include easily tuning foundation models with your own data, and enabling more people to build with generative AI.

Here are some key features of Gemini:

Prompt design is a process of trial and error, but there are principles and strategies to help you get the desired response from a generative AI model. You can change the response "temperature" to elicit a more creative response.

A prompt is a natural language request sent to a generative AI model to elicit a response back, and it can contain text, images, videos, audio, documents, and other modalities or even multiple modalities (multimodal). You can use Vertex AI Studio's prompt management tool to help you manage your prompts.

To nudge the model to behave in the desired way, you can use prompt design principles and strategies. This can help you simplify your prompts and reduce the cost and latency of your requests. Model tuning can help you customize the default behavior of Google's foundation models so that they consistently generate the desired results without using complex prompts.

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You can adapt models to your use case with prompt design. This process involves iterating on the prompt through a familiar chat interface and adjusting responses to achieve the desired outcome.

Iterate on the prompt through a chat interface to refine your model's behavior. You can also choose from multiple ways to adjust responses, such as changing the response "temperature" to elicit a more creative response.

Prompt design is a process of trial and error, but there are principles and strategies to help you nudge the model to behave in the desired way. Vertex AI Studio offers a prompt management tool to help you manage your prompts.

To simplify your prompts, you can customize the default behavior of Google's foundation models through model tuning. This process reduces the cost and latency of your requests by allowing you to use simpler prompts.

You can configure generative parameters to customize the model behavior. These parameters can be adjusted to achieve the desired outcome.

Here are some ways to adjust generative parameters:

By experimenting with prompt design and adjusting generative parameters, you can fine-tune your model's behavior to achieve the desired outcome.

In Weaviate, you can configure a collection's generative model integration configuration from v1.25.23 and later versions.

To use a Google generative AI model, you can specify one of the available models for Weaviate to use. If no model is specified, the default model is used.

You can configure a Weaviate index to use a Google generative AI model as follows.

Vertex AI users need to provide the Google Cloud project ID in the collection configuration.

Starting from v1.25.23, v1.26.8, and v1.27.1, the collection's generative model integration configuration is mutable, allowing for updates to the collection configuration.

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Generative AI models need to learn how to perform new tasks to be useful in real-world applications. You can customize your model through model tuning on Vertex AI to achieve this.

To access external information, generative AI models need to be able to access information outside of their training data. This can be done using the grounding and function calling features on Vertex AI.

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Safety filters are necessary to block harmful content generated by generative AI models. Vertex AI has built-in safety features to promote responsible use of generative AI services.

Retrieval augmented generation (RAG) operations can be performed on Vertex AI after configuring the generative AI integration. This can be done using the single prompt or grouped task method.

The capabilities of generative AI models work together to generate content that you want. These capabilities include learning new tasks, accessing external information, and blocking harmful content.

Here are the key capabilities of generative AI models:

You can access a wide range of APIs on Vertex AI, including 40+ proprietary models and 80+ OSS and 3rd party models on Vertex AI's Model Garden.

With access to Google's foundation models as APIs, you can easily deploy these models to applications. This includes models like Gemma, a family of lightweight, state-of-the-art open models built from the same research and technology used to create the Gemini models.

Gemini Pro is now in the Vertex AI model garden, as is Gemini Ultra, but the latter is only available to select customers in private preview.

Here's a list of some of the APIs available on Vertex AI:

Getting started with a Generative AI Studio is an exciting journey, and having the right resources is key.

Pricing for generative AI varies by foundation models and APIs, so be sure to check the costs before diving in.

The tutorials, quickstarts, and labs available will help you learn at your own pace and get hands-on experience with the technology.

Generative AI Studio offers a variety of learning materials to suit different learning styles and preferences.

You can find tutorials, quickstarts, and labs to help you get started with generative AI.

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Google Vertex and Duet are powerful tools that can help you unlock the full potential of generative AI. Vertex AI is a unified platform for ML models and generative AI, and it's generally available as of December 13.

With Vertex AI, you can discover AI models, build search and conversation applications with low or no code, and even tune and customize Gemini with reinforcement learning from human feedback. The platform includes a model garden with over 130 AI models from Google, open source, and third parties.

Vertex AI with Gemini supports 38 spoken languages and can generate text in Spanish, as demonstrated by Nenshad Bardoliwalla, AI/ML product leader on the Vertex AI platform at Google Cloud. The platform also includes safety filters and up-to-the-minute real-world information from retrieval augmented generation and embedding.

Duet AI in Security Operations is another exciting tool that combines threat intelligence and security operations assisted by generative AI. It's generally available as of December 13 and can be accessed by existing Google Cloud Security Operations Enterprise and Enterprise Plus customers in the U.S. and Europe at no extra cost.

Here are some key features of Vertex AI and Duet AI:

Vertex AI with Gemini is a generative AI platform designed for enterprise developers. It helps developers discover AI models and build search and conversation applications with low or no code.

Over 130 AI models from Google, open source, and third parties are available in Vertex AI's model garden, including Gemini Pro ImagenMedLM and Mistral.

Vertex AI with Gemini supports tuning, including adapter tuning, fine-tuning, low-rank adaptation, prompt design, and step-by-step distillation. Developers can also use reinforcement learning from human feedback to customize Gemini.

The platform includes safety features such as grounding, citation checking, 18 kinds of safety filters, and up-to-the-minute real-world information from retrieval augmented generation and embedding.

Developers can add new capabilities to the models, such as extensions and functions. Extensions allow Gemini to connect to enterprise apps and systems to retrieve information or take action on behalf of the user. Functions enable developers to compare data in their app to data generated by Gemini to improve the quality of Gemini's answers.

Gemini supports 38 spoken languages and can generate text in languages such as Spanish.

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Duet AI for Developers is a powerful tool that can do code completion, code generation, code refactoring, documentation generation, unit test generation, application deployment, and operational diagnostics. It works in a wide variety of languages and interoperates with tools such as Visual Studio Code.

Duet AI for Developers will be free from December 13, 2023, to February 1, 2024. After that, interested developers will need to contact Google Cloud for pricing.

Duet AI in Security Operations combines threat intelligence and security operations assisted by generative AI. It allows developers to ask what threats might be occurring in natural language and receive a response in natural language.

Duet AI in Security Operations is available to existing Google Cloud Security Operations Enterprise and Enterprise Plus customers in the U.S. and Europe at no extra cost. Users of Google's SecOps platform, Chronicle, will have access to Duet AI at no cost until March 5, 2024.

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Here are some key features of Duet AI in Security Operations: