Build and Deploy Your Own Generative AI Apps

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Build and Deploy Your Own Generative AI Apps

Introduction (#introduction)

Welcome to day two of the big AI hackathon co-hosted by Google and Bigcommerce. In this session, we will be focusing on prototyping and deploying Generative AI apps. Our goal is to help you understand how to get started with building generative AI apps and taking them from the prototype stage to deployment.

Prototyping and Deploying Generative AI Apps (#prototyping-and-deploying-generative-ai-apps)

Prototyping and deploying generative AI apps involves the process of building or prototyping a generative AI app and then taking it to production or deployment. This session will guide you through the different steps involved in this process, including using Google's Gen AI capability, utilizing Model Garden, prototyping with Generative AI Studio, and deploying models with one click. We will also explore building applications with Jupyter Notebook and implementing text-to-image search using embeddings and a matching engine.

Using Google's Gen AI Capability (#using-googles-gen-ai-capability)

Google's Gen AI capability offers a wide range of tools and resources for building and deploying generative AI models. These include pre-trained models available through APIs, a generative AI studio for experimentation, and Model Garden, which provides access to Google's Large Language Models as well as open source models. By using these tools, developers can easily prototype and deploy generative AI apps without having to build models from scratch.

Getting Started with Model Garden (#getting-started-with-model-garden)

Model Garden is a comprehensive platform that offers access to Google's large language models and open source models. It provides developers with a one-stop shop for exploring and experimenting with different models. Developers can choose from a variety of models categorized based on modality and features. Model Garden also offers options for one-click deployment, allowing for easy integration of models into applications.

Prototyping with Generative AI Studio (#prototyping-with-generative-ai-studio)

Generative AI Studio is a user-friendly interface that allows developers to experiment with generative AI models. It provides a visual way to interact with models, allowing for quick prototyping and testing. Developers can input prompts or use pre-defined examples to generate text or code. The generated output can be used directly in applications by copying the provided code snippets.

Deploying Models with One Click (#deploying-models-with-one-click)

One-click deployment is a convenient option for deploying models in the Google Cloud platform. With just a few clicks, developers can deploy models using pre-defined configurations. This eliminates the need to worry about setting up infrastructure or coding from scratch. One-click deployment is available for both Google's models and open source models, making it easy for developers to get started with deployment.

Building Applications with Jupyter Notebook (#building-applications-with-jupiter-notebook)

Jupyter Notebook provides a fully managed environment for building and testing applications. Developers can create notebooks and write code using Python. Jupyter Notebook integrates seamlessly with other Google Cloud services, allowing for easy interaction with data and models. Developers can prototype and deploy their applications using Jupyter Notebook, making it a powerful tool for building generative AI apps.

Text-to-Image Search with Embeddings and Matching Engine (#text-to-image-search-with-embeddings-and-matching-engine)

Text-to-image search is a popular use case for generative AI apps, particularly in the e-commerce industry. By using embeddings and a matching engine, developers can build powerful search engines that can match textual queries with corresponding images. This allows for a seamless and intuitive search experience for users. Developers can use Google's Vertex AI and matching engine to implement text-to-image search and provide users with accurate and Relevant search results.

Conclusion (#conclusion)

In conclusion, prototyping and deploying generative AI apps can be a complex but rewarding process. By leveraging the tools and resources offered by Google's Gen AI capability, developers can easily prototype and deploy generative AI models. Whether using Model Garden, Generative AI Studio, one-click deployment, or Jupyter Notebook, developers have various options for building and deploying generative AI apps. Additionally, text-to-image search using embeddings and a matching engine can provide powerful search capabilities for applications. With the right tools and techniques, developers can create innovative and impactful generative AI apps that enhance user experiences.

Highlights

Google's Gen AI capability offers a range of tools for prototyping and deploying generative AI apps
Model Garden provides access to Google's large language models and open source models
Generative AI Studio allows for quick prototyping and experimentation with AI models
One-click deployment simplifies the process of deploying models in the Google Cloud platform
Jupyter Notebook is a powerful tool for building and testing generative AI applications
Text-to-image search using embeddings and a matching engine enables accurate and relevant search results

FAQ

Q: What is Google's Gen AI capability?\ A: Google's Gen AI capability is a set of tools and resources that allow developers to prototype and deploy generative AI apps. It includes pre-trained models, APIs, Model Garden, Generative AI Studio, and one-click deployment options.

Q: How can I get started with prototyping a generative AI app?\ A: To get started with prototyping a generative AI app, you can use Model Garden, Generative AI Studio, or Jupyter Notebook. These platforms provide various options for building and testing AI models.

Q: Can I deploy generative AI models with just one click?\ A: Yes, Google provides one-click deployment options that simplify the process of deploying generative AI models. This eliminates the need to set up infrastructure or write complex code.

Q: How can I implement text-to-image search in my application?\ A: Text-to-image search can be implemented using embeddings and a matching engine. By converting text queries into vectors and searching for similar vectors in an index of images, accurate search results can be achieved.

Q: What are some benefits of using Jupyter Notebook for building generative AI apps?\ A: Jupyter Notebook provides a fully managed environment for building and testing generative AI applications. It allows developers to write code in Python and seamlessly integrate with other Google Cloud services.

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